Evaluation of Historical CMIP6 Model Simulations of Seasonal Mean Temperature over Pakistan during 1970–2014

The global decline in the sensitivity of vegetation productivity to precipitation from 2001 to 2018

The sensitivity of vegetation productivity to precipitation (S_ppt ) is a key metric for understanding the variations in vegetation productivity under changing precipitation and predicting future changes in ecosystem functions. However, a comprehensive assessment of S_ppt over all the global land is lacking. Here, we investigated spatial patterns and temporal changes of S_ppt across the global land from 2001 to 2018 with multiple streams of satellite observations. We found consistent spatial patterns of S_ppt with different satellite products: S_ppt was highest in dry regions while low in humid regions. Grassland and shrubland showed the highest S_ppt , and evergreen needle-leaf forest and wetland showed the lowest. Temporally, S_ppt showed a generally declining trend over the past two decades (p < .05), yet with clear spatial heterogeneities. The decline in S_ppt was especially noticeable in North America and Europe, likely due to the increase in precipitation. In central Russia and Australia, however, S_ppt showed an increasing trend. Biome-wise, most ecosystem types exhibited significant decrease in S_ppt , while grassland, evergreen broadleaf forest, and mixed forest showed slight increases or non-significant changes in S_ppt . Our finding of the overall decline in S_ppt implies a potential stabilization mechanism for ecosystem productivity under climate change. However, the revealed S_ppt increase for some regions and ecosystem types, in particular global grasslands, suggests that grasslands might be increasingly vulnerable to climatic variability with continuing global climate change.

Trends in Temperature, Precipitation, Potential Evapotranspiration, and Water Availability across the Teesta River Basin under 1.5 and 2 °C Temperature Rise Scenarios of CMIP6

Considering the linkages between climate change and water management, a lack of effort has been observed in analyzing the imprints of climate change over the transboundary Teesta river basin, where the changing climatic conditions can trigger substantial changes in eco-hydrological and socio-politico-economic setups. Therefore, to stimulate effective basin management, we investigated the trends in temperature, precipitation, potential evapotranspiration, and water availability under 1.5 and 2 °C warming levels across the transboundary Teesta river basin. The ensemble median of five bias-corrected model outputs from the Coupled Model Intercomparison Project Phase 6 (CMIP6) was used for this purpose. The results indicate that the temperature is expected to significantly increase (decrease) in the near (far) future, along with an overall significant increasing trend in monsoon precipitation. The evaporation paradox is found in the near future, and the water availability is likely to increase, with some exceptions for the pre-monsoon season. The perpetuation of such changes might result in environmental degradation through snow melting, glacial recession, and floods. Anticipating the changing climatic scenarios and their possible impacts, in this study, we recommend a variety of short- and long-term strategies for the concerned stakeholders to implement the Sustainable Development Goal 13, i.e., “Climate Action”, over the Teesta river basin.

Assessment of Sectoral Virtual Water Flows and Future Water Requirement in Agriculture Under SSP-RCP Scenarios: Reflections for Water Resources Management in Zhangye City

Water scarcity is a core issue that constraints the high-quality development of arid areas in northwestern China. Zhangye is an oasis city located in the Heihe River Basin in northwestern China. It is populated with an agriculture-dominated economy and faces more and more serious water crises. Virtual water is an indicator that can measure the embodied water in the traded products, which has been widely applied for making rational policies for water resources management. In addition, clarifying water requirements in agricultural sectors under future climate change scenarios is essential to develop more appropriate adaptation strategies. From this perspective, this study aims to evaluate and compare virtual water flows among various sectors in Zhangye for the years 2012 and 2017 with a single regional input-output model and to further clarify the future water requirement tendency in agriculture during 2020–2050 under different shared socioeconomic pathways and representative concentration pathways (SSP-RCP) scenarios. The results showed that the planting sector directly contributed most of the total water consumption with the highest direct coefficient of 3307.5 m3/yuan in 2012, whereas the manufacture of food products and tobacco processing sector had the largest proportion of indirect water consumption (99%) mainly from intermediate inputs of agricultural products. Water consumption intensity of all sectors on average decreased by 22% during 2012–2017, indicating an increasing water utilization efficiency in economic industries. Household consumption also can improve water utilization efficiency as the major pathway for final consumption (86.4% in 2017). Water scarcity in Zhangye was becoming increasingly prominent since virtual water net exports were higher than local consumption, especially in the agriculture, manufacturing, and energy supply industries. Moreover, under climate change scenarios, we found the highest level of water requirement per unit area occurred in 2000, but it still had an incremental potential by 2050, especially in SSP585. The high requirement intensity and large-scale maize planting caused a rising tendency of total crop water requirement with an annual increasing rate of 8.4% from 1980 to 2050. This makes it possible to adapt to climate change through scientific management measures and technical means. We further made policy implications for adaptive management of water resources in Zhangye.

Evaluating the Applicability of a Quantile–Quantile Adjustment Approach for Downscaling Monthly GCM Projections to Site Scale over the Qinghai-Tibet Plateau

In the context of global climate change, the Qinghai-Tibetan plateau (QTP) has experienced unprecedented changes in its local climate. While general circulation models (GCM) are able to forecast global-scale future climate change trends, further work needs to be done to develop techniques to apply GCM-predicted trends at site scale to facilitate local ecohydrological response studies. Given the QTP’s unique altitude-controlled climate pattern, the applicability of the quantile–quantile (Q-Q) adjustment approach for this purpose remains largely unknown and warrants investigation. In this study, this approach was evaluated at 36 sites to ensure the results are representative of different climatic and surface conditions on the QTP. Considering the practical needs of QTP studies, the study aims to assess its capability for downscaling monthly GCM simulations of major variables onto the site scale, including precipitation, air temperature, wind speed, relative humidity, and air pressure, based on two GCMs. The calibrated projections at the sites were verified against the observations and compared with those from two commonly used adjustment methods—the quantile-mapping method and the delta method. The results show that the general trends of most variables considered are well adjusted at all sites, with a quantile pair of 25–75% for all the variables except precipitation where 10–90% is used. The calibrated results are generally close to the observed values, with the best performance in air pressure, followed by air temperature and relative humidity. The performance is relatively limited in adjusting wind speed and precipitation. The accuracies decline as the adjustment extends into the future; a wider adjustment window may help increase the performance for the variables subject to climate changes. It is found that the performance of the adjustment is generally independent of the locations and seasons, but is strongly determined by the quality of GCM simulations. The Q-Q adjustment works better for the meteorological variables with fewer fluctuations and daily extremes. Variables with more similarities in probability density functions between the observations and GCM simulations tend to perform better in adjustment. Generally, this approach outperforms the two peer methods with broader applicability and higher accuracies for most major variables.

Future Changes in Precipitation Extremes over East Africa Based on CMIP6 Models

This paper presents an analysis of projected precipitation extremes over the East African region. The study employs six indices defined by the Expert Team on Climate Change Detection Indices to evaluate extreme precipitation. Observed datasets and Coupled Model Intercomparison Project Phase six (CMIP6) simulations are employed to assess the changes during the two main rainfall seasons: March to May (MAM) and October to December (OND). The results show an increase in consecutive dry days (CDD) and decrease in consecutive wet days (CWD) towards the end of the 21st century (2081–2100) relative to the baseline period (1995–2014) in both seasons. Moreover, simple daily intensity (SDII), very wet days (R95 p), very heavy precipitation >20 mm (R20 mm), and total wet-day precipitation (PRCPTOT) demonstrate significant changes during OND compared to the MAM season. The spatial variation for extreme incidences shows likely intensification over Uganda and most parts of Kenya, while a reduction is observed over the Tanzania region. The increase in projected extremes may pose a serious threat to the sustainability of societal infrastructure and ecosystem wellbeing. The results from these analyses present an opportunity to understand the emergence of extreme events and the capability of model outputs from CMIP6 in estimating the projected changes. More studies are recommended to examine the underlying physical features modulating the occurrence of extreme incidences projected for relevant policies.

Multi-Decadal Variability and Future Changes in Precipitation over Southern Africa

The future planning and management of water resources ought to be based on climate change projections at relevant temporal and spatial scales. This work uses the new regional demarcation for Southern Africa (SA) to investigate the spatio-temporal precipitation variability and trends of centennial-scale observation and modeled data, based on datasets from the sixth phase of the Coupled Model Intercomparison Project (CMIP6). The study employs several statistical methods to rank the models according to their precipitation simulation ability. The Theil–Sen slope estimator is used to assess precipitation trends, with a Student’s t-test for the significance test. The comparison of observation and model historical data enables identification of the best-performing global climate models (GCMs), which are then employed in the projection analysis under two Shared Socioeconomic Pathways (SSPs): SSP2-4.5 and SSP5-8.5. The GCMs adequately capture the annual precipitation variation but with a general overestimation, especially over high-elevation areas. Most of the models fail to capture precipitation over the Lesotho-Eswatini area. The three best-performing GCMs over SA are FGOALS-g3, MPI-ESM1-2-HR and NorESM2-LM. The sub-regions demonstrate that precipitation trends cannot be generalized and that localized studies can provide more accurate findings. Overall, precipitation in the wet and dry seasons shows an initial increase during the near future over western and eastern SA, followed by a reduction in precipitation during the mid- and far future under both projection scenarios. Madagascar is expected to experience a decrease in precipitation amount throughout the twenty-first century.

Evaluation of the Performance of CMIP6 Models in Reproducing Rainfall Patterns over North Africa

This study assesses the performance of historical rainfall data from the Coupled Model Intercomparison Project phase 6 (CMIP6) in reproducing the spatial and temporal rainfall variability over North Africa. Datasets from Climatic Research Unit (CRU) and Global Precipitation Climatology Centre (GPCC) are used as proxy to observational datasets to examine the capability of 15 CMIP6 models’ and their ensemble in simulating rainfall during 1951–2014. In addition, robust statistical metrics, empirical cumulative distribution function (ECDF), Taylor diagram (TD), and Taylor skill score (TSS) are utilized to assess models’ performance in reproducing annual and seasonal and monthly rainfall over the study domain. Results show that CMIP6 models satisfactorily reproduce mean annual climatology of dry/wet months. However, some models show a slight over/under estimation across dry/wet months. The models’ overall top ranking from all the performance analyses ranging from mean cycle simulation, trend analysis, inter-annual variability, ECDFs, and statistical metrics are as follows: EC-Earth3-Veg, UKESM1-0-LL, GFDL-CM4, NorESM2-LM, IPSL-CM6A-LR, and GFDL-ESM4. The mean model ensemble outperformed the individual CMIP6 models resulting in a TSS ratio (0.79). For future impact studies over the study domain, it is advisable to employ the multi-model ensemble of the best performing models.