Land-atmospheric feedbacks during droughts and heatwaves: state of the science and current challenges

Can Satellite-Based Thermal Anomalies Be Indicative of Heatwaves? An Investigation for MODIS Land Surface Temperatures in the Mediterranean Region

In recent years, an exceptional number of record-shattering temperature extremes have been observed, resulting in significant societal and environmental impacts. The Mediterranean region is particularly thermally vulnerable, frequently suffering from intense and severe heatwaves. Using daily temperature observations from 58 weather stations (NOAA Global Historical Climatology Network daily database) in the Mediterranean area, past heatwave episodes were initially detected. A daily LST time series was developed using Land Surface Temperature (LST) products from Moderate Resolution Imaging Spectroradiometer (MODIS) (Terra & Aqua satellites) for a 19-year period (2002–2020) at the station locations. LST anomalies were identified using percentile-based indices. It was found that remotely sensed-based LST presents the potential for understanding and monitoring heatwave events, as surface thermal anomalies were generally indicative of heatwaves. Approximately 42% (39%) of heatwave days during daytime (nighttime) coincided with LST anomalies; conversely, 51% of daytime LST anomalies overlapped with the exact days of a heatwave (38% at night). Importantly, the degree of association was significantly higher for extremely hot days (up to an 80% match) and long-lasting heatwaves (up to an 85% match). Rising trends in frequency and duration were observed for both heatwaves and LST anomalies. The results advance the understanding of surface-atmosphere coupling during extreme temperature days and reflect the suitability of thermal remote sensing in heatwave preparedness strategies.

Increased Compound Droughts and Heatwaves in a Double Pack in Central Asia

Compound droughts and heatwaves (CDHWs) are likely to cause more severe natural disasters than a single extreme event, and they have been exacerbated by rapid global warming. Based on high-resolution grid data, this study combines the daily-scale ERA5-Land dataset and the monthly-scale SPEI dataset with multiple indicators to analyze CDHWs. We calculated and analyzed the temporal and spatial modal distribution of CDHWs in Central Asia from 1981 to 2018, and in this paper, we discuss the sequence relationship between drought events, heatwave events, and CDHWs. The results show that the number of CDHWs in the study region have increased over time and expanded in terms of area, especially in eastern and southwestern Central Asia. The tsum (total frequency of CDHWs) was 0.5 times higher than the total heatwave frequency and it increased at a rate of 0.17/yr. The maximum duration of tmax (maximum duration of CDHWs in days) was 17 days. Furthermore, the occurrence rate of tmax was 96.67%, and the AH (CDHWs’ accumulated heat) had a rate of 97.78%, which, upon examination of the spatial trend pattern, accounted for the largest increase in terms of area. We also found that the TAH (CDHWs’ average temperature anomalies, SPEI < −0.5) shows obvious seasonality, with the increases in winter and spring being significantly greater than the increases in summer and autumn. The intensity of the CDHWs was stronger than that of a single extreme event, the temperature anomaly was higher than the average of 0.4–0.8 °C, and there was a north–south spatial pattern across the study region. In eastern and northwestern Central Asia, the AH and heatwaves (SPEI < −0.5) increased by 15–30 times per year on average. During the transition from the base period to the reference period, CDHWs increased by 25%, and the number of dry days prior to the CDHWs decreased by 7.35 days. The conclusion of our study can provide a theoretical basis for coping with climate change in arid zones.

Amplified risk of compound heat stress-dry spells in Urban India

Compound warm-dry spells over land, which is expected to occur more frequently and expected to cover a much larger spatial extent in a warming climate, result from the simultaneous or successive occurrence of extreme heatwaves, low precipitation, and synoptic conditions, e.g., low surface wind speeds. While changing patterns of weather and climate extremes cannot be ameliorated, effective mitigation requires an understanding of the multivariate nature of interacting drivers that influence the occurrence frequency and predictability of these extremes. However, risk assessments are often focused on univariate statistics, incorporating either extreme temperature or low precipitation; or at the most bivariate statistics considering concurrence of temperature versus precipitation, without accounting for synoptic conditions influencing their joint dependency. Based on station-based daily meteorological records from 23 urban and peri-urban locations of India, covering the 1970-2018 period, this study identifies four distinct regions that show temporal clustering of the timing of heatwaves. Further, combining joint probability distributions of interacting drivers, this analysis explored compound warm-dry potentials that result from the co-occurrence of warmer temperature, scarcer precipitation, and synoptic wind patterns. The results reveal 50-year severe heat stress solely based on the temperature at each location tends to be more frequent and is expected to become 5 to 17-year compound warm-dry events considering interdependence between attributes. Notably, considering dependence among drivers, a median 6-fold amplification (ranging from 3 to 10-fold) in compound warm-dry spell frequency is apparent relative to the expected annual number of a local (univariate) 50-year severe heatwave episode, indicating warming-induced desiccation is already underway over most of the urbanized areas of the country.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s00382-022-06324-y.

The 2021 western North America heat wave among the most extreme events ever recorded globally

In June 2021, western North America experienced a record-breaking heat wave outside the distribution of previously observed temperatures. While it is clear that the event was extreme, it is not obvious whether other areas in the world have also experienced events so far outside their natural variability. Using a novel assessment of heat extremes, we investigate how extreme this event was in the global context. Characterizing the relative intensity of an event as the number of standard deviations from the mean, the western North America heat wave is remarkable, coming in at over four standard deviations. Throughout the globe, where we have reliable data, only five other heat waves were found to be more extreme since 1960. We find that in both reanalyses and climate projections, the statistical distribution of extremes increases through time, in line with the distribution mean shift due to climate change. Regions that, by chance, have not had a recent extreme heat wave may be less prepared for potentially imminent events.

Satellite data reveal differential responses of Swiss forests to unprecedented 2018 drought

Extreme events such as the summer drought of 2018 in Central Europe are projected to occur more frequently in the future and may cause major damages including increased tree mortality and negative impacts on forest ecosystem services. Here, we quantify the response of >1 million forest pixels of 10 × 10 m across Switzerland to the 2018 drought in terms of resistance, recovery, and resilience. We used the Normalized Difference Water Index (NDWI) derived from Sentinel-2 satellite data as a proxy for canopy water content and analyzed its relative change. We calculated NDWI change between the 2017 pre-drought and 2018 drought years (indicating resistance), 2018 and the 2019 post-drought (indicating recovery), and between 2017-2019 (indicating resilience). Analyzing the data from this large natural experiment, we found that for 4.3% of the Swiss forest the NDWI declined between 2017 and 2018, indicating areas with low resistance of the forest canopy to drought effects. While roughly 50% of this area recovered, in 2.7% of the forested area NDWI continued to decline from 2018 to 2019, suggesting prolonged negative effects or delayed damage. We found differential forest responses to drought associated with site topographic characteristics and forest stand characteristics, and to a lesser extent with climatic conditions and interactions between these drivers. Low drought resistance and high recovery were most prominent at forest edges, but also on south-facing slopes and lower elevations. Tree functional type was the most important driver of drought resilience, with most of the damage in stands with high conifer abundance. Our results demonstrate the suitability of satellite-based quantification of drought-induced forest damage at high spatial resolution across large areas. Such information is important to predict how local site characteristics may impact forest vulnerability to future extreme events and help in the search for appropriate adaptation strategies.

Long-Term Dynamics and Response to Climate Change of Different Vegetation Types Using GIMMS NDVI3g Data over Amathole District in South Africa

Monitoring vegetation dynamics is essential for improving our understanding of how natural and managed agricultural landscapes respond to climate variability and change in the long term. Amathole District Municipality (ADM) in Eastern Cape Province of South Africa has been majorly threatened by climate variability and change during the last decades. This study explored long-term dynamics of vegetation and its response to climate variations using the satellite-derived normalized difference vegetation index from the third-generation Global Inventory Modeling and Mapping Studies (GIMMS NDVI3g) and the ERA5-Land global reanalysis product. A non-parametric trend and partial correlation analyses were used to evaluate the long-term vegetation changes and the role of climatic variables (temperature, precipitation, solar radiation and wind speed) during the period 1981–2015. The results of the ADM’s seasonal NDVI3g characteristics suggested that negative vegetation changes (browning trends) dominated most of the landscape from winter to summer while positive (greening) trends dominated in autumn during the study period. Much of these changes were reflected in forest landscapes with a higher coefficient of variation (CV ≈ 15) than other vegetation types (CV ≈ 10). In addition, the pixel-wise correlation analyses indicated a positive (negative) relationship between the NDVI3g and the ERA5-Land precipitation in spring–autumn (winter) seasons, while the reverse was the case with other climatic variables across vegetation types. However, the relationships between the NDVI3g and the climatic variables were relatively low (R < 0.5) across vegetation types and seasons, the results somewhat suggest the potential role of atmospheric variations in vegetation changes in ADM. The findings of this study provide invaluable insights into potential consequences of climate change and the need for well-informed decisions that underpin the evaluation and management of regional vegetation and forest resources.

A deep learning-based hybrid model of global terrestrial evaporation

Terrestrial evaporation (E) is a key climatic variable that is controlled by a plethora of environmental factors. The constraints that modulate the evaporation from plant leaves (or transpiration, Et) are particularly complex, yet are often assumed to interact linearly in global models due to our limited knowledge based on local studies. Here, we train deep learning algorithms using eddy covariance and sap flow data together with satellite observations, aiming to model transpiration stress (St), i.e., the reduction of Et from its theoretical maximum. Then, we embed the new St formulation within a process-based model of E to yield a global hybrid E model. In this hybrid model, the St formulation is bidirectionally coupled to the host model at daily timescales. Comparisons against in situ data and satellite-based proxies demonstrate an enhanced ability to estimate St and E globally. The proposed framework may be extended to improve the estimation of E in Earth System Models and enhance our understanding of this crucial climatic variable.

Drought self-propagation in drylands due to land-atmosphere feedbacks

Reduced evaporation due to dry soils can affect the land surface energy balance, with implications for local and downwind precipitation. When evaporation is constrained by soil moisture, the atmospheric supply of water is depleted, and this deficit may propagate in time and space. This mechanism could theoretically result in the self-propagation of droughts, but the extent to which this process occurs is unknown. Here we isolate the influence of soil moisture drought on downwind precipitation using Lagrangian moisture tracking constrained by observations from the 40 largest recent droughts worldwide. We show that dryland droughts are particularly prone to self-propagating, because evaporation tends to respond strongly to enhanced soil water stress. In drylands precipitation can decline by more than 15% due to upwind drought in during a single event, and up to 30% during individual months. In light of projected widespread reductions in water availability, this feedback may further exacerbate future droughts.

Analysis of trends, recurrences, severity and frequency of droughts using standardised precipitation index: Case of OR Tambo District Municipality, Eastern Cape, South Africa

South Africa is susceptible to droughts. However, little documentation exists on drought occurrence in South Africa at national, provincial and municipal administrative boundaries. This study profiles hydrological drought in OR Tambo District Municipality from 1998 to 2018, computing frequency, severity and intensity in order to show areas of high vulnerability. Data used were obtained from South African Weather Services. Standardised precipitation index (SPI) was calculated using the Meteorological Drought Monitor (MDM) software. Results showed a wide variation in monthly precipitation throughout the year. Coastal areas receive higher rainfall than inland municipalities. The study revealed that Nyandeni experienced the highest drought frequency of 62%, Mhlontlo (58%), King Sabatha Dalindyebo Municipality (57%), Ngquza Hill (55%) and Port St Johns Municipality showing the least at 52%. Hydrological drought severity frequency and duration varied between seven days and nine weeks. Drought intensity class exposed the annual average intensity for the five local municipalities represented as follows: KSDM (–0.71), PSJM (–0.99), Ngquza Hill (–0.81), Nyandeni (–0.71) and Mhlontlo (–0.62). The longest drought duration across OR Tambo was experienced in 2014 with durations varying from 3 to 11 weeks across the municipalities. OR Tambo District Municipality is susceptible to hydrological droughts and the extent varies across local municipalities. Results could be used for both adaptation planning and mitigating the impacts of future droughts. In addition, they could assist in guiding allocation of drought relief resources in ways that prioritise drought prone and vulnerable municipality.

Evaluation of Population—Food Relationship from the Perspective of Climate Productivity Potential: A Case Study of Eastern Gansu in Northwest China

Suffering from the double blow of the new crown pneumonia epidemic and floods, food security issues have once again become a source of concern. Eastern Gansu is an important dry farming area in northwestern China, and agricultural production has been greatly affected by climate change. Based on the climate data of 17 national meteorological stations in eastern Gansu from 1961 to 2020 and the data on population, grain planting area and grain production in each region from 1986 to 2019, using the Thornthwaite Memorial model, this paper analyzed the climate production potential (TSPV), population carrying capacity and population carrying capacity index in eastern Gansu, and then revealed the relationship between population and food in eastern Gansu. The findings and results revealed that: (1) over the past 60 years, the temperature in eastern Gansu has been increasing and precipitation has been decreasing; (2) TSPV has been increasing. Moreoever, the spatial distribution was significantly different, showing a trend of decreasing from the southeast to the northwest. Lintao, Huining, and Jingtai displayed a decreasing trend, while other areas exhibited an increasing trend. Precipitation was the main limiting factor for TSPV; (3) Grain production continued to increase due to changing hydrothermal conditions and improved production efficiency. Cultivated land–population carrying capacity and climate production potential–population carrying capacity (TSPV–population carrying capacity) both exhibited a significant increasing trend (p < 0.01). Cultivated land–population capacity increased from southeast to northwest, and all stations expressed an increasing trend. TSPV–population carrying capacity increased from southeast to northwest, and the whole region displayed an increasing trend. Even in extremely reduced production years, TSPV–population carrying capacity was also greater than cultivated land–population carrying capacity. This revealed that, under ideal conditions, TSPV–population carrying capacity can fully meet the needs of the current population. (4) The population carrying capacity index showed a significant downward trend (p < 0.01). It showed a trend of decreasing from south to north, and whole area underwent a decreasing trend consistently, indicating that the population–food relationship in eastern Gansu tended to be balanced. This result was conducive to correct assessment of the relationship between people and food in the study area, and provided a reference for formulating food policies.

Soil drought can mitigate deadly heat stress thanks to a reduction of air humidity

Global warming increases the number and severity of deadly heatwaves. Recent heatwaves often coincided with soil droughts that intensify air temperature but lower air humidity. Since lowering air humidity may reduce human heat stress, the net impact of soil desiccation on the morbidity and mortality of heatwaves remains unclear. Combining weather balloon and satellite observations, atmospheric modelling, and meta-analyses of heatwave mortality, we find that soil droughts—despite their warming effect—lead to a mild reduction in heatwave lethality. More specifically, morning dry soils attenuate afternoon heat stress anomaly by ~5%. This occurs because of reduced surface evaporation and increased entrainment of dry air aloft. The benefit appears more pronounced during specific events, such as the Chicago 1995 and Northern U.S. 2006 and 2012 heatwaves. Our findings suggest that irrigated agriculture may intensify lethal heat stress, and question recently proposed heatwave mitigation measures involving surface moistening to increase evaporative cooling.

Agricultural Drought Detection with MODIS Based Vegetation Health Indices in Southeast Germany

Droughts during the growing season are projected to increase in frequency and severity in Central Europe in the future. Thus, area-wide monitoring of agricultural drought in this region is becoming more and more important. In this context, it is essential to know where and when vegetation growth is primarily water-limited and whether remote sensing-based drought indices can detect agricultural drought in these areas. To answer these questions, we conducted a correlation analysis between the Normalized Difference Vegetation Index (NDVI) and Land Surface Temperature (LST) within the growing season from 2001 to 2020 in Bavaria (Germany) and investigated the relationship with land cover and altitude. In the second step, we applied the drought indices Temperature Condition Index (TCI), Vegetation Condition Index (VCI), and Vegetation Health Index (VHI) to primarily water-limited areas and evaluated them with soil moisture and agricultural yield anomalies. We found that, especially in the summer months (July and August), on agricultural land and grassland and below 800 m, NDVI and LST are negatively correlated and thus, water is the primary limiting factor for vegetation growth here. Within these areas and periods, the TCI and VHI correlate strongly with soil moisture and agricultural yield anomalies, suggesting that both indices have the potential to detect agricultural drought in Bavaria.

Population Exposure to Compound Droughts and Heatwaves in the Observations and ERA5 Reanalysis Data in the Gan River Basin, China

The frequency, duration, and magnitude of heatwaves and droughts are expected to increase in a warming climate, which can have profound impacts on the environment, society, and public health, and these may be severely affected specifically by compound droughts and heatwaves (CDHWs). On the basis of daily maximum temperature data and the one-month standardized precipitation evapotranspiration index (SPEI) from 1961 to 2018, the Gan River Basin (GRB) was taken as a case here to construct CDHW identification indicators and quantify the population exposure to CDHWs. We found that ERA5 reanalysis data performed well in overall simulating temperature, precipitation, one-month SPEI, heatwaves, and CDHWs in the GRB from 1961 to 2018. CDHWs during the period from 1997 to 2018 were slightly higher than that in 1961–1997. CDHWs were more likely to occur in the southern parts of the basin due to the relatively high values of drought–heatwave dependence indices. Atmospheric circulation analysis of the 2003 CDHW in the GRB showed a relatively long-lasting anomalous high pressure and anticyclonic circulation system, accompanied by the positive convective inhibition and surface net solar radiation anomalies. These circulating background fields eventually led to the exceptional 2003 CDHW occurrence in the GRB. The population exposure to CDHWs basically increased, especially for the moderate CDHWs in ERA5. The change in total exposure was mainly due to climate change. Compared with the period from 1989 to 1998, the contributions of the population change effect in 2009–2018 gradually increased with the increase in the CDHW magnitude both in the observations and ERA5 reanalysis data.

To what extent can rising [CO2 ] ameliorate plant drought stress?

Plant responses to elevated atmospheric carbon dioxide (eCO₂ ) have been hypothesized as a key mechanism that may ameliorate the impact of future drought. Yet, despite decades of experiments, the question of whether eCO₂ reduces plant water use, yielding 'water savings' that can be used to maintain plant function during periods of water stress, remains unresolved. In this Viewpoint, we identify the experimental challenges and limitations to our understanding of plant responses to drought under eCO₂ . In particular, we argue that future studies need to move beyond exploring whether eCO₂ played 'a role' or 'no role' in responses to drought, but instead more carefully consider the timescales and conditions that would induce an influence. We also argue that considering emergent differences in soil water content may be an insufficient means of assessing the impact of eCO₂ . We identify eCO₂ impact during severe drought (e.g. to the point of mortality), interactions with future changes in vapour pressure deficit and uncertainty about changes in leaf area as key gaps in our current understanding. New insights into CO₂  × drought interactions are essential to better constrain model theory that governs future climate model projections of land-atmosphere interactions during periods of water stress.

Upgrading Land-Cover and Vegetation Seasonality in the ECMWF Coupled System: Verification With FLUXNET Sites, METEOSAT Satellite Land Surface Temperatures, and ERA5 Atmospheric Reanalysis

In this study, we show that limitations in the representation of land cover and vegetation seasonality in the European Centre for Medium-Range Weather Forecasting (ECMWF) model are partially responsible for large biases (up to ∼10°C, either positive or negative depending on the region) on the simulated daily maximum land surface temperature (LST) with respect to satellite Earth Observations (EOs) products from the Land Surface Analysis Satellite Application Facility. The error patterns were coherent in offline land-surface and coupled land-atmosphere simulations, and in ECMWF's latest generation reanalysis (ERA5). Subsequently, we updated the ECMWF model's land cover characterization leveraging on state-of-the-art EOs-the European Space Agency Climate Change Initiative land cover data set and the Copernicus Global Land Services leaf area index. Additionally, we tested a clumping parameterization, introducing seasonality to the effective low vegetation coverage. The updates reduced the overall daily maximum LST bias and unbiased root-mean-squared errors. In contrast, the implemented updates had a neutral impact on daily minimum LST. Our results also highlighted the complex regional heterogeneities in the atmospheric sensitivity to land cover and vegetation changes, particularly with issues emerging over eastern Brazil and northeastern Asia. These issues called for a re-calibration of model parameters (e.g., minimum stomatal resistance, roughness length, rooting depth), along with a revision of several model assumptions (e.g., snow shading by high vegetation).

Moisture recycling and the potential role of forests as moisture source during European heatwaves

Heatwaves are extreme weather events that have become more frequent and intense in Europe over the past decades. Heatwaves are often coupled to droughts. The combination of them lead to severe ecological and socio-economic impacts. Heatwaves can self-amplify through internal climatic feedback that reduces local precipitation. Understanding the terrestrial sources of local precipitation during heatwaves might help identify mitigation strategies on land management and change that alleviate impacts. Moisture recycling of local water sources through evaporation allows a region to maintain precipitation in the same region or, by being transported by winds, in adjacent regions. To understand the role of terrestrial moisture sources for sustaining precipitation during heatwaves, we backtrack and analyse the precipitation sources of Northern, Western, and Southern sub-regions across Europe during 20 heatwave periods between 1979 and 2018 using the moisture tracking model Water Accounting Model-2layers (WAM-2layers). In Northern and Western Europe, we find that stabilizing anticyclonic patterns reduce the climatological westerly supply of moisture, mainly from the North Atlantic Ocean, and enhances the moisture flow from the eastern Euro-Asian continent and from within their own regions-suggesting over 10% shift of moisture supply from oceanic to terrestrial sources. In Southern Europe, limited local moisture sources result in a dramatic decrease in the local moisture recycling rate. Forests uniformly supply additional moisture to all regions during heatwaves and thus contribute to buffer local impacts. This study suggests that terrestrial moisture sources, especially forests, may potentially be important to mitigate moisture scarcity during heatwaves in Europe.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s00382-021-05921-7.

Underappreciated plant vulnerabilities to heat waves

With climate change, heat waves are becoming increasingly frequent, intense and broader in spatial extent. However, while the lethal effects of heat waves on humans are well documented, the impacts on flora are less well understood, perhaps except for crops. We summarize recent findings related to heat wave impacts including: sublethal and lethal effects at leaf and plant scales, secondary ecosystem effects, and more complex impacts such as increased heat wave frequency across all seasons, and interactions with other disturbances. We propose generalizable practical trials to quantify the critical bounding conditions of vulnerability to heat waves. Collectively, plant vulnerabilities to heat waves appear to be underappreciated and understudied, particularly with respect to understanding heat wave driven plant die-off and ecosystem tipping points.

Hydraulic failure and tree size linked with canopy die-back in eucalypt forest during extreme drought

Eastern Australia was subject to its hottest and driest year on record in 2019. This extreme drought resulted in massive canopy die-back in eucalypt forests. The role of hydraulic failure and tree size on canopy die-back in three eucalypt tree species during this drought was examined. We measured pre-dawn and midday leaf water potential (Ψleaf ), per cent loss of stem hydraulic conductivity and quantified hydraulic vulnerability to drought-induced xylem embolism. Tree size and tree health was also surveyed. Trees with most, or all, of their foliage dead exhibited high rates of native embolism (78-100%). This is in contrast to trees with partial canopy die-back (30-70% canopy die-back: 72-78% native embolism), or relatively healthy trees (little evidence of canopy die-back: 25-31% native embolism). Midday Ψleaf was significantly more negative in trees exhibiting partial canopy die-back (-2.7 to -6.3 MPa), compared with relatively healthy trees (-2.1 to -4.5 MPa). In two of the species the majority of individuals showing complete canopy die-back were in the small size classes. Our results indicate that hydraulic failure is strongly associated with canopy die-back during drought in eucalypt forests. Our study provides valuable field data to help constrain models predicting mortality risk.

An Assessment of the Impacts of Climate Variability and Change in KwaZulu-Natal Province, South Africa

Rainfall and air temperature variability pose the greatest risk to environmental change. Past trends in rainfall and air temperature facilitate projecting future climate changes for informed policy responses. We used a combination of the normalised difference vegetation index (NDVI) and observed data from 1968 to 2017 to assess changes in rainfall, moisture stress, and air temperature variability over time on bioclimatic regions of KwaZulu-Natal (KZN) Province, South Africa. Indicators used included consecutive dry days (CDDs), consecutive wet days (CWDs), very heavy rainfall days (R20), monthly maximum daily maximum air temperature (TXx), monthly minimum daily minimum air temperature (TNn), the total number of rainfall days, and monthly air temperature averages. Trends in rainfall and moisture stress are notable in different bioclimatic regions across the province. However, these trends are diverse, in general, and spatially different across and within the bioclimatic regions. Further, related rainfall indicators do not respond in the same way as would be expected. Air temperature trends were consistent with global trends and land–air temperature anomalies. Although daytime air temperatures showed a positive trend, extreme air temperature events and increases are predominant in inland regions. Night-time air temperatures showed an upward trend in most stations across KZN. Local weather-and-climate related characteristics are evolving due to climatic variability and change. The study shows that changes in climatic activities are detectable at a local level from existing historical weather data; therefore, adaptation strategies should be contextualised to respond to local and area-specific challenges.

Soil Moisture Estimation Synergy Using GNSS-R and L-Band Microwave Radiometry Data from FSSCat/FMPL-2

The Federated Satellite System mission (FSSCat) was the winner of the 2017 Copernicus Masters Competition and the first Copernicus third-party mission based on CubeSats. One of FSSCat’s objectives is to provide coarse Soil Moisture (SM) estimations by means of passive microwave measurements collected by Flexible Microwave Payload-2 (FMPL-2). This payload is a novel CubeSat based instrument combining an L1/E1 Global Navigation Satellite Systems-Reflectometer (GNSS-R) and an L-band Microwave Radiometer (MWR) using software-defined radio. This work presents the first results over land of the first two months of operations after the commissioning phase, from 1 October to 4 December 2020. Four neural network algorithms are implemented and analyzed in terms of different sets of input features to yield maps of SM content over the Northern Hemisphere (latitudes above 45° N). The first algorithm uses the surface skin temperature from the European Centre of Medium-Range Weather Forecast (ECMWF) in conjunction with the 16 day averaged Normalized Difference Vegetation Index (NDVI) from the Moderate Resolution Imaging Spectroradiometer (MODIS) to estimate SM and to use it as a comparison dataset for evaluating the additional models. A second approach is implemented to retrieve SM, which complements the first model using FMPL-2 L-band MWR antenna temperature measurements, showing a better performance than in the first case. The error standard deviation of this model referred to the Soil Moisture and Ocean Salinity (SMOS) SM product gridded at 36 km is 0.074 m3/m3. The third algorithm proposes a new approach to retrieve SM using FMPL-2 GNSS-R data. The mean and standard deviation of the GNSS-R reflectivity are obtained by averaging consecutive observations based on a sliding window and are further included as additional input features to the network. The model output shows an accurate SM estimation compared to a 9 km SMOS SM product, with an error of 0.087 m3/m3. Finally, a fourth model combines MWR and GNSS-R data and outperforms the previous approaches, with an error of just 0.063 m3/m3. These results demonstrate the capabilities of FMPL-2 to provide SM estimates over land with a good agreement with respect to SMOS SM.

Single-Pass Soil Moisture Retrieval Using GNSS-R at L1 and L5 Bands: Results from Airborne Experiment

Global Navigation Satellite System—Reflectometry (GNSS-R) has already proven its potential for retrieving a number of geophysical parameters, including soil moisture. However, single-pass GNSS-R soil moisture retrieval is still a challenge. This study presents a comparison of two different data sets acquired with the Microwave Interferometer Reflectometer (MIR), an airborne-based dual-band (L1/E1 and L5/E5a), multiconstellation (GPS and Galileo) GNSS-R instrument with two 19-element antenna arrays with four electronically steered beams each. The instrument was flown twice over the OzNet soil moisture monitoring network in southern New South Wales (Australia): the first flight was performed after a long period without rain, and the second one just after a rain event. In this work, the impact of surface roughness and vegetation attenuation in the reflectivity of the GNSS-R signal is assessed at both L1 and L5 bands. The work analyzes the reflectivity at different integration times, and finally, an artificial neural network is used to retrieve soil moisture from the reflectivity values. The algorithm is trained and compared to a 20-m resolution downscaled soil moisture estimate derived from SMOS soil moisture, Sentinel-2 normalized difference vegetation index (NDVI) data, and ECMWF Land Surface Temperature.

Large-scale commodity agriculture exacerbates the climatic impacts of Amazonian deforestation

In the Amazon rainforest, land use following deforestation is diverse and dynamic. Mounting evidence indicates that the climatic impacts of forest loss can also vary considerably, depending on specific features of the affected areas. The size of the deforested patches, for instance, was shown to modulate the characteristics of local climatic impacts. Nonetheless, the influence of different types of land use and management strategies on the magnitude of local climatic changes remains uncertain. Here, we evaluated the impacts of large-scale commodity farming and rural settlements on surface temperature, rainfall patterns, and energy fluxes. Our results reveal that changes in land-atmosphere coupling are induced not only by deforestation size but also, by land use type and management patterns inside the deforested areas. We provide evidence that, in comparison with rural settlements, deforestation caused by large-scale commodity agriculture is more likely to reduce convective rainfall and increase land surface temperature. We demonstrate that these differences are mainly caused by a more intensive management of the land, resulting in significantly lower vegetation cover throughout the year, which reduces latent heat flux. Our findings indicate an urgent need for alternative agricultural practices, as well as forest restoration, for maintaining ecosystem processes and mitigating change in the local climates across the Amazon basin.

Flash Drought Response to Precipitation and Atmospheric Evaporative Demand in Spain

Flash drought is the result of strong precipitation deficits and/or anomalous increases in atmospheric evaporative demand (AED), which triggers a rapid decline in soil moisture and stresses vegetation over short periods of time. However, little is known about the role of precipitation and AED in the development of flash droughts. For this paper, we compared the standardized precipitation index (SPI) based on precipitation, the evaporative demand drought index (EDDI) based on AED, and the standardized evaporation precipitation index (SPEI) based on the differences between precipitation and AED as flash drought indicators for mainland Spain and the Balearic Islands for 1961–2018. The results show large differences in the spatial and temporal patterns of flash droughts between indices. In general, there was a high degree of consistency between the flash drought patterns identified by the SPI and SPEI, with the exception of southern Spain in the summer. The EDDI showed notable spatial and temporal differences from the SPI in winter and summer, while it exhibited great coherence with the SPEI in summer. We also examined the sensitivity of the SPEI to AED in each month of the year to explain its contribution to the possible development of flash droughts. Our findings showed that precipitation is the main driver of flash droughts in Spain, although AED can play a key role in the development of these during periods of low precipitation, especially in the driest areas and in summer.

Drought effects on specific-cause mortality in Lisbon from 1983 to 2016: Risks assessment by gender and age groups

Portugal (Southwestern Europe) experiences a high incidence of dry hazards such as drought, a phenomenon that entails a notable burden of morbidity and mortality worldwide. For the first time in the Lisbon district, a time-series study was conducted to evaluate the impact of drought measured by the Standardised Precipitation Index (SPI) and Standardised Precipitation-Evapotranspiration Index (SPEI) on the daily natural, circulatory, and respiratory mortality from 1983 to 2016. An assessment by gender and adult age population groups (45-64, 65-74, ≥75 years old) was included. To estimate the relative risks and attributable risks, generalised linear models with a Poisson link were used. Additionally, the influence of heatwaves and atmospheric pollution for the period from 2007 to 2016 (available period for pollution data) was considered. The main findings indicate statistically significant associations between drought conditions and all analysed causes of mortality. Moreover, SPEI shows an improved capability to reflect the different risks. People in the 45-64 year-old group did not indicate any significant influence in any of the cases, whereas the oldest groups had the highest risk. The drought effects on mortality among the population varied across the different study periods, and in general, the men population was affected more than the women population (except for the SPEI and circulatory mortality during the long study period). The short-term influence of droughts on mortality could be explained primarily by the effect of heatwaves and pollution; however, when both gender and age were considered in the Poisson models, the effect of drought also remained statistically significant when all climatic phenomena were included for specific groups of the total population and men. This type of study facilitates a better understanding of the population at risk and allows the development of more effective measures to mitigate the drought effects on the population.

Kernel methods and their derivatives: Concept and perspectives for the earth system sciences

Kernel methods are powerful machine learning techniques which use generic non-linear functions to solve complex tasks. They have a solid mathematical foundation and exhibit excellent performance in practice. However, kernel machines are still considered black-box models as the kernel feature mapping cannot be accessed directly thus making the kernels difficult to interpret. The aim of this work is to show that it is indeed possible to interpret the functions learned by various kernel methods as they can be intuitive despite their complexity. Specifically, we show that derivatives of these functions have a simple mathematical formulation, are easy to compute, and can be applied to various problems. The model function derivatives in kernel machines is proportional to the kernel function derivative and we provide the explicit analytic form of the first and second derivatives of the most common kernel functions with regard to the inputs as well as generic formulas to compute higher order derivatives. We use them to analyze the most used supervised and unsupervised kernel learning methods: Gaussian Processes for regression, Support Vector Machines for classification, Kernel Entropy Component Analysis for density estimation, and the Hilbert-Schmidt Independence Criterion for estimating the dependency between random variables. For all cases we expressed the derivative of the learned function as a linear combination of the kernel function derivative. Moreover we provide intuitive explanations through illustrative toy examples and show how these same kernel methods can be applied to applications in the context of spatio-temporal Earth system data cubes. This work reflects on the observation that function derivatives may play a crucial role in kernel methods analysis and understanding.

High resolution climate change projections for the Pyrenees region

Abstract. The Pyrenees, located in the transition zone of Atlantic and Mediterranean climates, constitute a paradigmatic example of mountains undergoing rapid changes in environmental conditions, with potential impact on the availability of water resources, mainly for downstream populations. High-resolution probabilistic climate change projections for precipitation and temperature are a crucial element for stakeholders to make well-informed decisions on adaptation to new climate conditions. In this line, we have generated high–resolution climate projections for 21st century by applying two statistical downscaling methods (regression for max and min temperatures, and analogue for precipitation) over the Pyrenees region in the frame of the CLIMPY project over a new high-resolution (5 km × 5 km) observational grid using 24 climate models from CMIP5. The application of statistical downscaling to such a high resolution observational grid instead of station data partially circumvent the problems associated to the non-uniform distribution of observational in situ data. This new high resolution projections database based on statistical algorithms complements the widely used EUROCORDEX data based on dynamical downscaling and allows to identify features that are dependent on the particular downscaling method. In our analysis, we not only focus on maximum and minimum temperatures and precipitation changes but also on changes in some relevant extreme indexes, being 1986–2005 the reference period. Although climate models predict a general increase in temperature extremes for the end of the 21st century, the exact spatial distribution of changes in temperature and much more in precipitation remains uncertain as they are strongly model dependent. Besides, for precipitation, the uncertainty associated to models can mask – depending on the zones- the signal of change. However, the large number of downscaled models and the high resolution of the used grid allow us to provide differential information at least at massif level. The impact of the RCP becomes significant for the second half of the 21st century, with changes – differentiated by massifs – of extreme temperatures and analysed associated extreme indexes for RCP8.5 at the end of the century.

More frequent and widespread persistent compound drought and heat event observed in China

Compound drought and heat event (CDHE) causes severe impacts on agriculture, ecosystem, and human health. Based on daily maximum surface air temperature and meteorological drought composite index data in China, changing features of CDHEs in warm season from 1961 to 2018 is explored at a daily time scale based on a strict and objective definition in this study. Results reveal that CDHEs have occurred more frequently and widely in China, especially since the late 1990s. Notably, such changes are more obvious in Southwest China, eastern Northwest China, northern North China, and the coastal area of southeastern China. A prominent feature is that persistent CDHEs on a daily scale have increased significantly. To better understand climate change of compound extreme events, further studies on the physical mechanism, especially attribution analyses at a regional scale, are urgently needed.

A century of observations reveals increasing likelihood of continental-scale compound dry-hot extremes

Using over a century of ground-based observations over the contiguous United States, we show that the frequency of compound dry and hot extremes has increased substantially in the past decades, with an alarming increase in very rare dry-hot extremes. Our results indicate that the area affected by concurrent extremes has also increased significantly. Further, we explore homogeneity (i.e., connectedness) of dry-hot extremes across space. We show that dry-hot extremes have homogeneously enlarged over the past 122 years, pointing to spatial propagation of extreme dryness and heat and increased probability of continental-scale compound extremes. Last, we show an interesting shift between the main driver of dry-hot extremes over time. While meteorological drought was the main driver of dry-hot events in the 1930s, the observed warming trend has become the dominant driver in recent decades. Our results provide a deeper understanding of spatiotemporal variation of compound dry-hot extremes.

Rainfall manipulation experiments as simulated by terrestrial biosphere models: Where do we stand?

Changes in rainfall amounts and patterns have been observed and are expected to continue in the near future with potentially significant ecological and societal consequences. Modelling vegetation responses to changes in rainfall is thus crucial to project water and carbon cycles in the future. In this study, we present the results of a new model-data intercomparison project, where we tested the ability of 10 terrestrial biosphere models to reproduce the observed sensitivity of ecosystem productivity to rainfall changes at 10 sites across the globe, in nine of which, rainfall exclusion and/or irrigation experiments had been performed. The key results are as follows: (a) Inter-model variation is generally large and model agreement varies with timescales. In severely water-limited sites, models only agree on the interannual variability of evapotranspiration and to a smaller extent on gross primary productivity. In more mesic sites, model agreement for both water and carbon fluxes is typically higher on fine (daily-monthly) timescales and reduces on longer (seasonal-annual) scales. (b) Models on average overestimate the relationship between ecosystem productivity and mean rainfall amounts across sites (in space) and have a low capacity in reproducing the temporal (interannual) sensitivity of vegetation productivity to annual rainfall at a given site, even though observation uncertainty is comparable to inter-model variability. (c) Most models reproduced the sign of the observed patterns in productivity changes in rainfall manipulation experiments but had a low capacity in reproducing the observed magnitude of productivity changes. Models better reproduced the observed productivity responses due to rainfall exclusion than addition. (d) All models attribute ecosystem productivity changes to the intensity of vegetation stress and peak leaf area, whereas the impact of the change in growing season length is negligible. The relative contribution of the peak leaf area and vegetation stress intensity was highly variable among models.

Plant profit maximization improves predictions of European forest responses to drought

Knowledge of how water stress impacts the carbon and water cycles is a key uncertainty in terrestrial biosphere models. We tested a new profit maximization model, where photosynthetic uptake of CO₂ is optimally traded against plant hydraulic function, as an alternative to the empirical functions commonly used in models to regulate gas exchange during periods of water stress. We conducted a multi-site evaluation of this model at the ecosystem scale, before and during major droughts in Europe. Additionally, we asked whether the maximum hydraulic conductance in the soil-plant continuum k_max (a key model parameter which is not commonly measured) could be predicted from long-term site climate. Compared with a control model with an empirical soil moisture function, the profit maximization model improved the simulation of evapotranspiration during the growing season, reducing the normalized mean square error by c. 63%, across mesic and xeric sites. We also showed that k_max could be estimated from long-term climate, with improvements in the simulation of evapotranspiration at eight out of the 10 forest sites during drought. Although the generalization of this approach is contingent upon determining k_max , it presents a mechanistic trait-based alternative to regulate canopy gas exchange in global models.

Atmospheric heat and moisture transport to energy- and water-limited ecosystems

The land biosphere is a crucial component of the Earth system that interacts with the atmosphere in a complex manner through manifold feedback processes. These relationships are bidirectional, as climate affects our terrestrial ecosystems, which, in turn, influence climate. Great progress has been made in understanding the local interactions between the terrestrial biosphere and climate, but influences from remote regions through energy and water influxes to downwind ecosystems remain less explored. Using a Lagrangian trajectory model driven by atmospheric reanalysis data, we show how heat and moisture advection affect gross carbon production at interannual scales and in different ecoregions across the globe. For water‐limited regions, results show a detrimental effect on ecosystem productivity during periods of enhanced heat and reduced moisture advection. These periods are typically associated with winds that disproportionately come from continental source regions, as well as positive sensible heat flux and negative latent heat flux anomalies in those upwind locations. Our results underline the vulnerability of ecosystems to the occurrence of upwind climatic extremes and highlight the importance of the latter for the spatiotemporal propagation of ecosystem disturbances.

Regional Climate Impacts of Irrigation in Northern Italy Using a High Resolution Model

Irrigation is crucial for sustaining agriculture in certain regions; however, there are effects on the local climate. Previous studies discussed that the irrigation signal might depend on the geographical region as well as the synoptic and climatic conditions. The work presented here aims to investigate the mechanisms behind changes in the irrigation impact on the local conditions depending on synoptic changes. Different to previous works, this employs convection-permitting simulations. Irrigation processes are parameterized in three different ways depending on the evaporative loss. The region of focus is in northern Italy (Po Valley), which is of interest for both the soil-atmosphere coupling strength and widely used irrigation. The simulation period is Summer 2015 (May–July), which includes a heatwave month (July) and an average month (June). The results show how irrigation prevented the drying out of the soil layers during the heatwave. This influences the surface flux partition differently, by increasing moisture flux and decreasing the sensible heat flux. In general, the irrigation impact magnitude, with respect to the control simulation, is more than double in July compared to June. This study discusses climate implications for the region, such as the impact of widespread irrigation on the vegetation health, the heatwave feedback mechanism, atmospheric pollution, and human heat discomfort.

A Precipitation Recycling Network to Assess Freshwater Vulnerability: Challenging the Watershed Convention

Water resources and water scarcity are usually regarded as local aspects for which a watershed-based management appears adequate. However, precipitation, as a main source of freshwater, may depend on moisture supplied through land evaporation from outside the watershed. This notion of evaporation as a local "green water" supply to precipitation is typically not considered in hydrological water assessments. Here we propose the concept of a watershed precipitation recycling network, which establishes atmospheric pathways and links land surface evaporation as a moisture supply to precipitation, hence contributing to local but also remote freshwater resources. Our results show that up to 74% of summer precipitation over European watersheds depends on moisture supplied from other watersheds, which contradicts the conventional consideration of autarkic watersheds. The proposed network approach illustrates atmospheric pathways and enables the objective assessment of freshwater vulnerability and water scarcity risks under global change. The illustrated watershed interdependence emphasizes the need for global water governance to secure freshwater availability.

Retrospective Analysis of Summer Temperature Anomalies with the Use of Precipitation and Evapotranspiration Rates

Drought and extreme temperatures forecasting is important for water management and the prevention of health risks, especially in a period of observed climatic change. A large precipitation deficit together with increased evapotranspiration rates in the preceding days contribute to exceptionally high temperature anomalies in the summer above the average local maximum temperature for each month. Using a retrospective approach, this study investigated droughts and extreme temperatures in the greater area of Nicosia, Cyprus and suggests a different approach in determining the lag period of summer temperature anomalies and precipitation. In addition, dry conditions defined with the use of the Standardized Precipitation-Evapotranspiration Index (SPEI) were associated with positive temperature anomalies at a percentage up to 33.7%. The compound effect of precipitation levels and evapotranspiration rates of the preceding days for the period 1988–2017 to summer temperature anomalies was demonstrated with significantly statistical R squared values up to 0.57. Furthermore, the cooling effect of precipitation was higher and prolonged longer in rural and suburban than urban areas, a fact that is directly related to the evaporation potential of the area in concern. Our work demonstrates the compound effect of precipitation levels and evapotranspiration rates of the preceding days to summer temperature anomalies.

Summer high temperature extremes over Northeastern China predicted by spring soil moisture

Current seasonal climate predictions mainly reside in the ocean anomalies. However, the prediction skills are generally limited over many extra-tropical land areas where the oceanic effects are relatively weak. In this study, we address the potential of preceding spring soil moisture condition to predict summer hot days over Northeastern China, a typical Northern Hemisphere mid-latitude region. The results show that spring soil moisture condition over Central-Eastern China is closely related with following summer hot days over Northeastern China for the period of 1979-2017. The statistical model based on the preceding spring soil moisture condition yields temporal cross-validated correlation skill of 0.57 for summer hot days over Northeastern China. The spatial pattern correlation skills of independent hindcast experiments for 2009-2017 are also high, ranging from 0.87 to 0.94. Our results can be easily applied to practical prediction of summer hot days over Northeastern China, and help to provide better climate services and reduce the detrimental effects of extreme heat over this extra-tropical region.

Concurrent 2018 Hot Extremes Across Northern Hemisphere Due to Human-Induced Climate Change

Extremely high temperatures pose an immediate threat to humans and ecosystems. In recent years, many regions on land and in the ocean experienced heat waves with devastating impacts that would have been highly unlikely without human-induced climate change. Impacts are particularly severe when heat waves occur in regions with high exposure of people or crops. The recent 2018 spring-to-summer season was characterized by several major heat and dry extremes. On daily average between May and July 2018 about 22% of the populated and agricultural areas north of 30° latitude experienced concurrent hot temperature extremes. Events of this type were unprecedented prior to 2010, while similar conditions were experienced in the 2010 and 2012 boreal summers. Earth System Model simulations of present-day climate, that is, at around +1 °C global warming, also display an increase of concurrent heat extremes. Based on Earth System Model simulations, we show that it is virtually certain (using Intergovernmental Panel on Climate Change calibrated uncertainty language) that the 2018 north hemispheric concurrent heat events would not have occurred without human-induced climate change. Our results further reveal that the average high-exposure area projected to experience concurrent warm and hot spells in the Northern Hemisphere increases by about 16% per additional +1 °C of global warming. A strong reduction in fossil fuel emissions is paramount to reduce the risks of unprecedented global-scale heat wave impacts.

Compound Droughts and Heat Waves in China

Droughts and heat waves both are natural extreme climate events occurring in most parts of the world. To understand the spatio-temporal characteristics of droughts and heat waves in China, we examine changes in droughts, heat waves, and the compound of both during 1961–2017 based on high resolution gridded monthly sc_PDSI and daily temperature data. Results show that North China and Northwest China are the two regions that experience the most frequent droughts, while Central China is the least drought-affected region. Significant drought decreasing trends were mostly observed Qinghai, Xinjiang, and Tibet provinces, while the belt region between Yunnan and Heilongjiang provinces experienced significant drought increasing trends. Heat waves occur more frequently than droughts, and the increase of heat wave occurrence is also more obvious. The increasing of heat wave occurrence since the 2000s has been unprecedented. The compound droughts and heat waves were mild from the 1960s to 1980s, and began to increase in 1990s. Furthermore, the significant increasing trends of the percentage of compound droughts and heat waves to droughts are observed in entire China, and more than 90% drought occurrences are accompanied by one or more heat waves in the 2010s. The results highlight the increased percentage of compound droughts and heat waves and call for improved efforts on assessing the impact of compound extremes, especially in an era of changing climate.

Advances in the Remote Sensing of Terrestrial Evaporation

Characterizing the terrestrial carbon, water and energy cycles depends strongly on a capacity to accurately reproduce the spatial and temporal dynamics of land surface evaporation. For this, and many other reasons, monitoring terrestrial evaporation across multiple space and time scales has been an area of focused research for many decades. Much of this activity has been supported by developments in satellite remote sensing, which have been leveraged to deliver new process insights, model development and methodological improvements. In this Special Issue, published contributions explored a range of research topics directed towards the enhanced estimation of terrestrial evaporation. Here we summarize these cutting-edge efforts and provide an overview of some of the state-of-the-art approaches for retrieving this key variable. Some perspectives on outstanding challenges, issues, and opportunities are also presented.

Exploring the Potential of Satellite Solar-Induced Fluorescence to Constrain Global Transpiration Estimates

The opening and closing of plant stomata regulates the global water, carbon and energy cycles. Biophysical feedbacks on climate are highly dependent on transpiration, which is mediated by vegetation phenology and plant responses to stress conditions. Here, we explore the potential of satellite observations of solar-induced chlorophyll fluorescence (SIF)—normalized by photosynthetically-active radiation (PAR)—to diagnose the ratio of transpiration to potential evaporation (‘transpiration efficiency’, τ). This potential is validated at 25 eddy-covariance sites from seven biomes worldwide. The skill of the state-of-the-art land surface models (LSMs) from the eartH2Observe project to estimate τ is also contrasted against eddy-covariance data. Despite its relatively coarse (0.5°) resolution, SIF/PAR estimates, based on data from the Global Ozone Monitoring Experiment 2 (GOME-2) and the Clouds and Earth’s Radiant Energy System (CERES), correlate to the in situ τ significantly (average inter-site correlation of 0.59), with higher correlations during growing seasons (0.64) compared to decaying periods (0.53). In addition, the skill to diagnose the variability of in situ τ demonstrated by all LSMs is on average lower, indicating the potential of SIF data to constrain the formulations of transpiration in global models via, e.g., data assimilation. Overall, SIF/PAR estimates successfully capture the effect of phenological changes and environmental stress on natural ecosystem transpiration, adequately reflecting the timing of this variability without complex parameterizations.

Towards Estimating Land Evaporation at Field Scales Using GLEAM

The evaporation of water from land into the atmosphere is a key component of the hydrological cycle. Accurate estimates of this flux are essential for proper water management and irrigation scheduling. However, continuous and qualitative information on land evaporation is currently not available at the required spatio-temporal scales for agricultural applications and regional-scale water management. Here, we apply the Global Land Evaporation Amsterdam Model (GLEAM) at 100 m spatial resolution and daily time steps to provide estimates of land evaporation over The Netherlands, Flanders, and western Germany for the period 2013–2017. By making extensive use of microwave-based geophysical observations, we are able to provide data under all weather conditions. The soil moisture estimates from GLEAM at high resolution compare well with in situ measurements of surface soil moisture, resulting in a median temporal correlation coefficient of 0.76 across 29 sites. Estimates of terrestrial evaporation are also evaluated using in situ eddy-covariance measurements from five sites, and compared to estimates from the coarse-scale GLEAM v3.2b, land evaporation from the Satellite Application Facility on Land Surface Analysis (LSA-SAF), and reference grass evaporation based on Makkink’s equation. All datasets compare similarly with in situ measurements and differences in the temporal statistics are small, with correlation coefficients against in situ data ranging from 0.65 to 0.95, depending on the site. Evaporation estimates from GLEAM-HR are typically bounded by the high values of the Makkink evaporation and the low values from LSA-SAF. While GLEAM-HR and LSA-SAF show the highest spatial detail, their geographical patterns diverge strongly due to differences in model assumptions, model parameterizations, and forcing data. The separate consideration of rainfall interception loss by tall vegetation in GLEAM-HR is a key cause of this divergence: while LSA-SAF reports maximum annual evaporation volumes in the Green Heart of The Netherlands, an area dominated by shrubs and grasses, GLEAM-HR shows its maximum in the national parks of the Veluwe and Heuvelrug, both densely-forested regions where rainfall interception loss is a dominant process. The pioneering dataset presented here is unique in that it provides observational-based estimates at high resolution under all weather conditions, and represents a viable alternative to traditional visible and infrared models to retrieve evaporation at field scales.

Sensitivity of Evapotranspiration Components in Remote Sensing-Based Models

Accurately estimating evapotranspiration (ET) at large spatial scales is essential to our understanding of land-atmosphere coupling and the surface balance of water and energy. Comparisons between remote sensing-based ET models are difficult due to diversity in model formulation, parametrization and data requirements. The constituent components of ET have been shown to deviate substantially among models as well as between models and field estimates. This study analyses the sensitivity of three global ET remote sensing models in an attempt to isolate the error associated with forcing uncertainty and reveal the underlying variables driving the model components. We examine the transpiration, soil evaporation, interception and total ET estimates of the Penman-Monteith model from the Moderate Resolution Imaging Spectroradiometer (PM-MOD), the Priestley-Taylor Jet Propulsion Laboratory model (PT-JPL) and the Global Land Evaporation Amsterdam Model (GLEAM) at 42 sites where ET components have been measured using field techniques. We analyse the sensitivity of the models based on the uncertainty of the input variables and as a function of the raw value of the variables themselves. We find that, at 10% added uncertainty levels, the total ET estimates from PT-JPL, PM-MOD and GLEAM are most sensitive to Normalized Difference Vegetation Index (NDVI) (%RMSD = 100.0), relative humidity (%RMSD = 122.3) and net radiation (%RMSD = 7.49), respectively. Consistently, systemic bias introduced by forcing uncertainty in the component estimates is mitigated when components are aggregated to a total ET estimate. These results suggest that slight changes to forcing may result in outsized variation in ET partitioning and relatively smaller changes to the total ET estimates. Our results help to explain why model estimates of total ET perform relatively well despite large inter-model divergence in the individual ET component estimates.

Subcontinental heat wave triggers terrestrial and marine, multi-taxa responses

Heat waves have profoundly impacted biota globally over the past decade, especially where their ecological impacts are rapid, diverse, and broad-scale. Although usually considered in isolation for either terrestrial or marine ecosystems, heat waves can straddle ecosystems of both types at subcontinental scales, potentially impacting larger areas and taxonomic breadth than previously envisioned. Using climatic and multi-species demographic data collected in Western Australia, we show that a massive heat wave event straddling terrestrial and maritime ecosystems triggered abrupt, synchronous, and multi-trophic ecological disruptions, including mortality, demographic shifts and altered species distributions. Tree die-off and coral bleaching occurred concurrently in response to the heat wave, and were accompanied by terrestrial plant mortality, seagrass and kelp loss, population crash of an endangered terrestrial bird species, plummeting breeding success in marine penguins, and outbreaks of terrestrial wood-boring insects. These multiple taxa and trophic-level impacts spanned >300,000 km²-comparable to the size of California-encompassing one terrestrial Global Biodiversity Hotspot and two marine World Heritage Areas. The subcontinental multi-taxa context documented here reveals that terrestrial and marine biotic responses to heat waves do not occur in isolation, implying that the extent of ecological vulnerability to projected increases in heat waves is underestimated.

New Insights on Land Surface-Atmosphere Feedbacks over Tropical South America at Interannual Timescales

We present a simplified overview of land-atmosphere feedbacks at interannual timescales over tropical South America as structural sets of linkages among surface air temperature (T), specific humidity at 925 hPa (q925), volumetric soil water content (Θ), precipitation (P), and evaporation (E), at monthly scale during 1979–2010. Applying a Maximum Covariance Analysis (MCA), we identify the modes of greatest interannual covariability in the datasets. Time series extracted from the MCAs were used to quantify linear and non-linear metrics at up to six-month lags to establish connections among variables. All sets of metrics were summarized as graphs (Graph Theory) grouped according to their highest ENSO-degree association. The core of ENSO-activated interactions is located in the Amazon River basin and in the Magdalena-Cauca River basin in Colombia. Within the identified multivariate structure, Θ enhances the interannual connectivity since it often exhibits two-way feedbacks with the whole set of variables. That is, Θ is a key variable in defining the spatiotemporal patterns of P and E at interannual time-scales. For both the simultaneous and lagged analysis, T activates non-linear associations with q925 and Θ. Under the ENSO influence, T is a key variable to diagnose the dynamics of interannual feedbacks of the lower troposphere and soil interfaces over tropical South America. ENSO increases the interannual connectivity and memory of the feedback mechanisms.