Incorporating model quality information in climate change detection and attribution studies

Climate change causes declines and greater extremes in wetland inundation in a region important for wetland birds

Wetland ecosystems are vital for maintaining global biodiversity, as they provide important stopover sites for many species of migrating wetland-associated birds. However, because weather determines their hydrologic cycles, wetlands are highly vulnerable to effects of climate change. Although changes in temperature and precipitation resulting from climate change are expected to reduce inundation of wetlands, few efforts have been made to quantify how these changes will influence the availability of stopover sites for migratory wetland birds. Additionally, few studies have evaluated how climate change will influence interannual variability or the frequency of extremes in wetland availability. For spring and fall bird migration in seven ecoregions in the south-central Great Plains of North America, we developed predictive models associating abundance of inundated wetlands with a suite of weather and land cover variables. We then used these models to generate predictions of wetland inundation at the end of the century (2069-2099) under future climate change scenarios. Climate models predicted the average number of inundated wetlands will likely decline during both spring and fall migration periods, with declines being greatest in the eastern ecoregions of the southern Great Plains. However, the magnitude of predicted declines varied considerably across climate models and ecoregions, with uncertainty among climate models being greatest in the High Plains ecoregion. Most ecoregions also were predicted to experience more-frequent extremely dry years (i.e., years with extremely low wetland abundances), but the projected change in interannual variability of wetland inundation was relatively small and varied across ecoregions and seasons. Because the south-central Great Plains represents an important link along the migratory routes of many wetland-dependent avian species, future declines in wetland inundation and more frequent periods of only a few wetlands being inundated will result in an uncertain future for migratory birds as they experience reduced availability of wetland stopover habitat across their migration pathways.

Future changes in the precipitation regime over the Arabian Peninsula with special emphasis on UAE: insights from NEX-GDDP CMIP6 model simulations

Global warming can profoundly influence the mean climate over the Arabian Peninsula, which may significantly influence both natural and human systems. The present study aims to investigate the changes in the precipitation regime in response to climate change over the Arabian Peninsula, with special emphasis on the United Arab Emirates (UAE). This work is performed using a sub-set of high-resolution NASA Earth Exchange Global Daily Downscaled Projections (NEX-GDDP) data derived from Coupled Model Intercomparison Project Phase 6 (CMIP6) Global Climate Models under three different Shared Socioeconomic Pathway (SSP) scenarios (SSP1-2.6, SSP2-4.5, and SSP5-8.5). The changes are analyzed in three phases such as 2021-2050 (near future), 2051-2080 (mid future) and 2080-2100 (far future), with the period of 1985-2014 as the baseline. This study represents the first attempt to utilize data from NEX-GDDP models to project the regional patterns of precipitation regime across the Arabian Peninsula. Results suggest that the annual precipitation is expected to increase over most of the UAE by up to 30%, particularly intense from the mid-future onwards in all scenarios. Specifically, the spatiotemporal distribution of precipitation extremes such as intensity, 1-day highest precipitation, and precipitation exceeding 10 mm days are increasing; in contrast, the consecutive dry days may decrease towards the end of the century. The results show that the changes in extreme precipitation under a warming scenario relative to the historical period indicate progressive wetting across UAE, accompanied by increased heavy precipitation events and reduced dry spell events, particularly under the high emission scenarios. A high-resolution dataset is essential for a better understanding of changes in precipitation patterns, especially in regions where more detailed information is needed on a local scale to achieve water, food security, and environmental sustainability to formulate effective adaptation strategies for mitigating the potential risks and consequences associated with variations in wet and dry conditions.

Anthropogenic influence of temperature changes across East Asia using CMIP6 simulations

The present study explores the impact of anthropogenic forcings (ANT) on surface air temperatures (SATs) across East Asia (EA) over a long period (1850–2014) using the new Coupled Model Intercomparison Project Phase 6 (CMIP6) datasets. Based on CMIP6 multi-model ensemble simulations, the historical simulations (twentieth century) and future (twenty-first century) SAT projections were investigated. Our calculations show that during 1850–2014, the combination of ANT and natural (NAT) (‘ALL = ANT + NAT’) forcings increased the EA’s SAT by 0.031 °C/decade, while a high increase of 0.08 °C/decade due to greenhouse gas (GHG) emissions. The ANT forcing rapidly increased after 1969. As a result, SAT change was enhanced at a rate of 0.268 °C/decade and 0.255 °C/decade due to GHG and ALL forcings, respectively. Human-induced GHG emissions were the dominant factors driving SAT warming and will also contribute to substantial future warming trends. Additionally, the optimal fingerprinting method was used to signify the influence of ANT forcing on climate change in EA. In a two-signal analysis, the ANT forcing was distinctly detected and detached from NAT forcing. In three-signal analyses, GHG forcing was dominant and separated from AER and NAT forcings. The future projections from 2015 to 2100 were examined based on CMIP6 socioeconomic pathway emission scenarios.

Assessing and predicting soil carbon density in China using CMIP5 earth system models

Soil carbon (SC) is a key component of the carbon cycle and plays an important role in climate change; however, quantitatively assessing SC dynamics at the regional scale remains challenging. Earth system model (ESM) that considers multiple environmental factors and spatial heterogeneity has become a powerful tool to explore carbon cycle-climate feedbacks, although the performance of the ESM is diverse and highly uncertain. Thus, identifying reliable ESMs is a prerequisite for better understanding the response of SC dynamics to human activity and climate change. The 16 ESMs that participated in the fifth phase of the Coupled Model Intercomparison Project (CMIP5) were employed to evaluate the skill performance of SC density simulation by comparison with reference data from the International Geosphere-Biosphere Programme Data and Information System (IGBP-DIS). Although ESMs generally reflect spatial patterns with lower SC in northwest China and higher SC in southeast China, 11 of 16 ESMs underestimated the SC in China, and 5 of 16 ESMs overestimated the SC density as most ESMs had large discrepancies in capturing the SC density in the northern high latitudes of China and the Qinghai-Tibet Plateau. According to a series of model performance statistics, SC simulated by Institute Pierre Simon Laplace (IPSL) Coupled Model had a close spatial pattern with IGBP-DIS and showed higher skills for SC predictions in China relative to other CMIP5 ESMs. The multimodel ensemble average obtained by IPSL family ESMs showed that SC density exhibited increasing trends under both the RCP4.5 scenario and RCP8.5 scenario. The SC density increased slowly under RCP8.5 compared with that under RCP4.5 and even displayed a decreasing trend in the late 21st century. The findings of this study can provide a reference for identifying the shortcomings of SC predictions in China and guide SC parameterization improvement in ESMs.

Robust detection of forced warming in the presence of potentially large climate variability

Climate warming is unequivocal and exceeds internal climate variability. However, estimates of the magnitude of decadal-scale variability from models and observations are uncertain, limiting determination of the fraction of warming attributable to external forcing. Here, we use statistical learning to extract a fingerprint of climate change that is robust to different model representations and magnitudes of internal variability. We find a best estimate forced warming trend of 0.8°C over the past 40 years, slightly larger than observed. It is extremely likely that at least 85% is attributable to external forcing based on the median variability across climate models. Detection remains robust even when evaluated against models with high variability and if decadal-scale variability were doubled. This work addresses a long-standing limitation in attributing warming to external forcing and opens up opportunities even in the case of large model differences in decadal-scale variability, model structural uncertainty, and limited observational records.

Evaluation and Projection of Near-Surface Wind Speed over China Based on CMIP6 Models

The characteristics of near-surface wind speed (NWS) are important to the study of dust storms, evapotranspiration, heavy rainfall, air pollution, and wind energy development. This study evaluated the performance of 30 models of the Coupled Model Intercomparison Project Phase 6 (CMIP6) through comparison with observational NWS data acquired in China during a historical period (1975–2014), and projected future changes in NWS under three scenarios (SSP1-2.6, SSP2-4.5, and SSP5-8.5) based on an optimal multi-model ensemble. Results showed that most models reproduced the spatial pattern of NWS for all seasons and the annual mean, although the models generally overestimated NWS magnitude. All models tended to underestimate the trends of decline of NWS for all seasons and the annual mean. On the basis of a comprehensive ranking index, the KIOST-ESM, CNRM-ESM2-1, HadGEM3-GC31-LL, CMCC-CM2-SR5, and KACE-1-0-G models were ranked as the five best-performing models. In the projections of future change, nationally averaged NWS for all months was weaker than in the historical period, and the trends decreased markedly under all the different scenarios except the winter time series under SSP2-4.5. Additionally, the projected NWS over most regions of China weakened in both the early period (2021–2060) and the later period (2061–2100).

High-resolution dynamically downscaled rainfall and temperature projections for ecological life zones within Puerto Rico and for the US Virgin Islands

The Weather Research and Forecasting (WRF) model and a combination of the Regional Spectral Model (RSM) and the Japanese Meteorological Agency Non-Hydrostatic Model (NHM) were used to dynamically downscale selected CMIP5 global climate models to provide 2-km projections with hourly model output for Puerto Rico and the U.S. Virgin Islands. Two 20-year time slices were downscaled for historical (1986-2005) and future (2041-2060) periods following RCP8.5. Projected changes to mean and extreme temperature and precipitation were quantified for Holdridge life zones within Puerto Rico and for the U.S. Virgin Islands. The evaluation reveals a persistent cold bias for all islands in the U.S. Caribbean, a dry bias across Puerto Rico, and a wet bias on the windward side of mountains within the U.S. Virgin Islands. Despite these biases, model simulations show a robust drying pattern for all islands that is generally larger for Puerto Rico (25% annual rainfall reduction for some life zones) than the U.S. Virgin Islands (12% island average). The largest precipitation reductions are found during the more convectively active afternoon and evening hours. Within Puerto Rico, the model uncertainty increases for the wetter life zones, especially for precipitation. Across the life zones, both models project unprecedented maximum and minimum temperatures that may exceed 200 days annually above the historical baseline with only small changes to the frequency of extreme rainfall. By contrast, in the U.S. Virgin Islands, there is no consensus on the location of the largest drying relative to the windward and leeward side of the islands. However, the models project the largest increases in maximum temperature on the southern side of St. Croix and in higher elevations of St. Thomas and St. John.

Recent Advances on Metalloporphyrin-Based Materials for Visible-Light-Driven CO2 Reduction

Photocatalytic CO₂ reduction is a promising technology to mitigate environmental issue and the energy crisis. The four nitrogen atoms in the porphyrin ring can incorporate transition metals to form stable active sites for CO₂ activation and photoreduction. Nevertheless, the photocatalytic efficiency of metalloporphyrins is still low due to the insufficient photoelectron injection to drive CO₂ photoreduction upon visible light irradiation. To address this issue, considerable efforts have been made to introduce photosensitizers for constructing homogeneous or heterogeneous metalloporphyrin-based photocatalytic systems. In this Review, recent advances of metalloporphyrin-based materials for visible-light-driven CO₂ reduction were summarized. The methods for the modulation of photosensitizing process at molecular level were presented for the promotion of photocatalytic performance. The mechanism of CO₂ activation and photocatalytic conversion was illustrated. Better insight into the structure-activity relationship provides guidance to the design of metalloporphyrin-related photocatalytic systems.

Physics-guided probabilistic modeling of extreme precipitation under climate change

Earth System Models (ESMs) are the state of the art for projecting the effects of climate change. However, longstanding uncertainties in their ability to simulate regional and local precipitation extremes and related processes inhibit decision making. Existing state-of-the art approaches for uncertainty quantification use Bayesian methods to weight ESMs based on a balance of historical skills and future consensus. Here we propose an empirical Bayesian model that extends an existing skill and consensus based weighting framework and examine the hypothesis that nontrivial, physics-guided measures of ESM skill can help produce reliable probabilistic characterization of climate extremes. Specifically, the model leverages knowledge of physical relationships between temperature, atmospheric moisture capacity, and extreme precipitation intensity to iteratively weight and combine ESMs and estimate probability distributions of return levels. Out-of-sample validation suggests that the proposed Bayesian method, which incorporates physics-guidance, has the potential to derive reliable precipitation projections, although caveats remain and the gain is not uniform across all cases.

Robust assessment of the time of emergence of precipitation change in West Africa

The time of emergence (TOE) of climate change is defined as the time when a new climate state emerges from a prior one. TOE assessment is particularly relevant in West Africa, a region highly threatened by climate change and urgently needing trustworthy climate predictions. In this paper, the TOE of precipitation change in West Africa is assessed for the first time, by analyzing 6 precipitation metrics (cumulated precipitation, number of wet and very wet days, onset and length of the rainy season) computed from the output of 29 state-of-the-art climate models. In West Sahel, climate conditions characterized by reduced occurrence of wet days are likely to emerge before 2036, leading to the possible emergence of a dryer climate in 2028–2052. In East Sahel, a wetter precipitation regime characterized by increased occurrence of very wet days is likely to emerge before 2054. Results do not provide a clear indication about a possible climate shift in the onset and length of the rainy season. Although uncertainty in climate model future projections still limits the robust determination of TOE locally, this study provides reliable time constraints to the expected climate shift in West Africa at the sub-regional scale, supporting adaptation measures to the future change in the precipitation regime.

Detection and Attribution of Atmospheric Precipitable Water Changes since the 1970s over China

Atmospheric water vapor increases as air temperature rises, which causes further warming. Thus, understanding the underlying causes of atmospheric water vapor change is vital in climate change research. Here, we conducted detection and attribution analyses of atmospheric precipitable water (PW) changes from 1973-2012 over China using an optimal fingerprinting method by comparing the homogenized radiosonde humidity data with CMIP5 model simulations. Results show that the increase in water vapor can be largely attributed to human activities. The effect of anthropogenic forcing (ANT) can be robustly detected and separated from the response to the natural external forcing (NAT) in the two-signal analysis. The moistening attributable to the ANT forcing explains most of the observed PW increase, while the NAT forcing leads to small moistening. GHGs are the primary moistening contributor responsible for the anthropogenic climate change, and the effect of GHGs can be also clearly detected and successfully attributed to the observed PW increases in a three-signal analysis. The scaling factor is used to adjust the CMIP5 model-projected PW changes over China and the observation-constrained future projections suggest that atmospheric water vapor may increase faster (slower) than that revealed by the raw simulations over whole (eastern) China.

Evaluation and Projection of Extreme Precipitation over Northern China in CMIP5 Models

This study evaluates 32 climate models from CMIP5 compared with a daily gridded observation dataset of extreme precipitation indices including total extreme precipitation (R95p), maximum consecutive five days of precipitation (RX5day) and wet days larger than 10 mm of precipitation (R10mm) over Northern China during the historical period (1986–2005). Results show the majority models have good performance on spatial distribution but overestimate the amplitude of precipitation over Northern China. Most models can also capture interannual variation of R95p and RX5d, but with poor simulations on R10mm. Considering both spatial and temporal factors, the best multi-model ensemble (Group 1) has been selected and improved by 42%, 34%, and 37% for R95p, RX5d, and R10mm, respectively. Projection of extreme precipitation indicates that the fastest-rising region is in Northwest China due to the enhanced rainfall intensity. However, the uncertainty analysis shows the increase of extreme rainfall over Northwest China has a low confidence level. The projection of increasing extreme rainfall over Northeast China from Group 1 due to the longer extreme rainfall days is more credible. The weak subtropical high and southwest winds from Arabian Sea lead to the low wet biases from Group 1 and the cyclonic anomalies over Northeast China, which result in more extreme precipitation.

Comparison Analysis of Total Precipitable Water of Satellite-Borne Microwave Radiometer Retrievals and Island Radiosondes

Total precipitable water (TPW) of satellite-borne microwave radiometer retrievals is compared with the data that were collected from 49 island radiosonde stations for the period 2007–2015. Great consistency was found between TPW measurements made by radiosonde and eight satellite-borne microwave radiometers, including SSMI-F13, SSMI-F14, SSMIS-F16, SSMIS-F17, AMSR-E, AMSR-2, GMI, and WindSat. Mean values of the TPW differences for eight satellites ranged from −0.51 to 0.38mm, both root mean square errors and standard deviations were around 3mm, and all of the correlation coefficients between satellite TPW retrievals and radiosonde TPW for each satellite can reach 0.99. Subsequently, an analysis of the comparison results was conducted, which revealed three problems in the satellite TPW retrieval and two problems in radiosonde data. For TPW retrievals of satellite, when the values are above 60 mm, the precision of TPW retrieval significantly decreases with a distinct dry bias, which can reach 4 mm; additionally, abias related to wind speed and the uncertainty with the TPW retrieval in the presence of rain, which is stronger than 1mm/h, was found. The TPW measurements of radiosonde made by the type of IM-MK3 from India were quite unreliable, and almost all of the radiosonde data during the daytime were plagued by a dry bias.

Human influence on the seasonal cycle of tropospheric temperature

We provide scientific evidence that a human-caused signal in the seasonal cycle of tropospheric temperature has emerged from the background noise of natural variability. Satellite data and the anthropogenic "fingerprint" predicted by climate models show common large-scale changes in geographical patterns of seasonal cycle amplitude. These common features include increases in amplitude at mid-latitudes in both hemispheres, amplitude decreases at high latitudes in the Southern Hemisphere, and small changes in the tropics. Simple physical mechanisms explain these features. The model fingerprint of seasonal cycle changes is identifiable with high statistical confidence in five out of six satellite temperature datasets. Our results suggest that attribution studies with the changing seasonal cycle provide powerful evidence for a significant human effect on Earth's climate.

Projected wetland densities under climate change: habitat loss but little geographic shift in conservation strategy

Climate change poses major challenges for conservation and management because it alters the area, quality, and spatial distribution of habitat for natural populations. To assess species' vulnerability to climate change and target ongoing conservation investments, researchers and managers often consider the effects of projected changes in climate and land use on future habitat availability and quality and the uncertainty associated with these projections. Here, we draw on tools from hydrology and climate science to project the impact of climate change on the density of wetlands in the Prairie Pothole Region of the USA, a critical area for breeding waterfowl and other wetland-dependent species. We evaluate the potential for a trade-off in the value of conservation investments under current and future climatic conditions and consider the joint effects of climate and land use. We use an integrated set of hydrological and climatological projections that provide physically based measures of water balance under historical and projected future climatic conditions. In addition, we use historical projections derived from ten general circulation models (GCMs) as a baseline from which to assess climate change impacts, rather than historical climate data. This method isolates the impact of greenhouse gas emissions and ensures that modeling errors are incorporated into the baseline rather than attributed to climate change. Our work shows that, on average, densities of wetlands (here defined as wetland basins holding water) are projected to decline across the U.S. Prairie Pothole Region, but that GCMs differ in both the magnitude and the direction of projected impacts. However, we found little evidence for a shift in the locations expected to provide the highest wetland densities under current vs. projected climatic conditions. This result was robust to the inclusion of projected changes in land use under climate change. We suggest that targeting conservation towards wetland complexes containing both small and relatively large wetland basins, which is an ongoing conservation strategy, may also act to hedge against uncertainty in the effects of climate change.

On the causal structure between CO2 and global temperature

We use a newly developed technique that is based on the information flow concept to investigate the causal structure between the global radiative forcing and the annual global mean surface temperature anomalies (GMTA) since 1850. Our study unambiguously shows one-way causality between the total Greenhouse Gases and GMTA. Specifically, it is confirmed that the former, especially CO₂, are the main causal drivers of the recent warming. A significant but smaller information flow comes from aerosol direct and indirect forcing, and on short time periods, volcanic forcings. In contrast the causality contribution from natural forcings (solar irradiance and volcanic forcing) to the long term trend is not significant. The spatial explicit analysis reveals that the anthropogenic forcing fingerprint is significantly regionally varying in both hemispheres. On paleoclimate time scales, however, the cause-effect direction is reversed: temperature changes cause subsequent CO₂/CH₄ changes.

Identifying human influences on atmospheric temperature

We perform a multimodel detection and attribution study with climate model simulation output and satellite-based measurements of tropospheric and stratospheric temperature change. We use simulation output from 20 climate models participating in phase 5 of the Coupled Model Intercomparison Project. This multimodel archive provides estimates of the signal pattern in response to combined anthropogenic and natural external forcing (the fingerprint) and the noise of internally generated variability. Using these estimates, we calculate signal-to-noise (S/N) ratios to quantify the strength of the fingerprint in the observations relative to fingerprint strength in natural climate noise. For changes in lower stratospheric temperature between 1979 and 2011, S/N ratios vary from 26 to 36, depending on the choice of observational dataset. In the lower troposphere, the fingerprint strength in observations is smaller, but S/N ratios are still significant at the 1% level or better, and range from three to eight. We find no evidence that these ratios are spuriously inflated by model variability errors. After removing all global mean signals, model fingerprints remain identifiable in 70% of the tests involving tropospheric temperature changes. Despite such agreement in the large-scale features of model and observed geographical patterns of atmospheric temperature change, most models do not replicate the size of the observed changes. On average, the models analyzed underestimate the observed cooling of the lower stratosphere and overestimate the warming of the troposphere. Although the precise causes of such differences are unclear, model biases in lower stratospheric temperature trends are likely to be reduced by more realistic treatment of stratospheric ozone depletion and volcanic aerosol forcing.

Building climate resilience in the Blue Nile/Abay Highlands: a role for Earth system sciences

The Blue Nile (Abay) Highlands of Ethiopia are characterized by significant interannual climate variability, complex topography and associated local climate contrasts, erosive rains and erodible soils, and intense land pressure due to an increasing population and an economy that is almost entirely dependent on smallholder, low-input agriculture. As a result, these highland zones are highly vulnerable to negative impacts of climate variability. As patterns of variability and precipitation intensity alter under anthropogenic climate change, there is concern that this vulnerability will increase, threatening economic development and food security in the region. In order to overcome these challenges and to enhance sustainable development in the context of climate change, it is necessary to establish climate resilient development strategies that are informed by best-available Earth System Science (ESS) information. This requirement is complicated by the fact that climate projections for the Abay Highlands contain significant and perhaps irreducible uncertainties. A critical challenge for ESS, then, is to generate and to communicate meaningful information for climate resilient development in the context of a highly uncertain climate forecast. Here we report on a framework for applying ESS to climate resilient development in the Abay Highlands, with a focus on the challenge of reducing land degradation.