A reexamination of the dry gets drier and wet gets wetter paradigm over global land: insight from terrestrial water storage changes

The “dry gets drier and wet gets wetter” (DDWW) paradigm has been widely used to summarize the expected trends of the global hydrologic cycle under climate change. However, the paradigm is challenged over land due to different measures and datasets, and is still unexplored from the perspective of terrestrial water storage anomaly (TWSA). Considering the essential role of TWSA in wetting and drying of the land surface, here we built upon a large ensemble of TWSA datasets including satellite-based products, global hydrological models, land surface models, and global climate models to evaluate the DDWW hypothesis during the historical (1985–2014) and future (2071–2100) periods under various scenarios. We find that 27.1 % of global land confirms the DDWW paradigm, while 22.4 % of the area shows the opposite pattern during the historical period. In the future, the DDWW paradigm is still challenged with the percentage supporting the pattern lower than 20 %, and both the DDWW-validated and DDWW-opposed proportion increase along with the intensification of emission scenarios. Our findings will provide insights and implications for global wetting and drying trends from the perspective of TWSA under climate change.

Climate Change Impacts on Groundwater Recharge in Cold and Humid Climates: Controlling Processes and Thresholds

Long-term changes in precipitation and temperature indirectly impact aquifers through groundwater recharge (GWR). Although estimates of future GWR are needed for water resource management, they are uncertain in cold and humid climates due to the wide range in possible future climatic conditions. This work aims to (1) simulate the impacts of climate change on regional GWR for a cold and humid climate and (2) identify precipitation and temperature changes leading to significant long-term changes in GWR. Spatially distributed GWR is simulated in a case study for the southern Province of Quebec (Canada, 36,000 km2) using a water budget model. Climate scenarios from global climate models indicate warming temperatures and wetter conditions (RCP4.5 and RCP8.5; 1951–2100). The results show that annual precipitation increases of >+150 mm/yr or winter precipitation increases of >+25 mm will lead to significantly higher GWR. GWR is expected to decrease if the precipitation changes are lower than these thresholds. Significant GWR changes are produced only when the temperature change exceeds +2 °C. Temperature changes of >+4.5 °C limit the GWR increase to +30 mm/yr. This work provides useful insights into the regional assessment of future GWR in cold and humid climates, thus helping in planning decisions as climate change unfolds. The results are expected to be comparable to those in other regions with similar climates in post-glacial geological environments and future climate change conditions.

Net irrigation requirement under different climate scenarios using AquaCrop over Europe

Global soil water availability is challenged by the effects of climate change and a growing population. On average 70 % of freshwater extraction is attributed to agriculture, and the demand is increasing. In this study, the effects of climate change on the evolution of the irrigation water requirement to sustain current crop productivity are assessed by using the FAO crop growth model AquaCrop version 6.1. The model is run at 0.5° lat × 0.5° lon resolution over the European mainland, assuming a general C3-type of crop, and forced by climate input data from the Inter-Sectoral Impact Model Intercomparison Project phase three (ISIMIP3). First, the performance of AquaCrop surface soil moisture (SSM) simulations using historical meteorological input from two ISIMIP3 forcing datasets is evaluated with satellite-based SSM estimates. When driven by ISIMIP3a reanalysis meteorology for the years 2011–2016, daily simulated SSM values have an unbiased root-mean-square difference of 0.08 and 0.06 m3m−3 with SSM retrievals from the Soil Moisture Ocean Salinity (SMOS) and Soil Moisture Active Passive (SMAP) missions, respectively. When forced with ISIMIP3b meteorology from five Global Climate Models (GCM) for the years 2011–2020, the historical simulated SSM climatology closely agrees with the climatology of the reanalysis-driven AquaCrop SSM climatology as well as the satellite-based SSM climatologies. Second, the evaluated AquaCrop model is run to quantify the future irrigation requirement, for an ensemble of five GCMs and three different emission scenarios. The simulated net irrigation requirement (Inet) of the three summer months for a near and far future climate period (2031–2060 and 2071–2100) is compared to the baseline period of 1985–2014, to assess changes in the mean and interannual variability of the irrigation demand. Averaged over the continent and the model ensemble, the far future Inet is expected to increase by 67 mm year–1 (+30 %) under a high emission scenario Shared Socioeconomic Pathway (SSP) 3-7.0. Central and southern Europe are the most impacted with larger Inet increases. The interannual variability of Inet is likely to increase in northern and central Europe, whereas the variability is expected to decrease in southern regions. Under a high mitigation scenario (SSP1-2.6), the increase in Inet will stabilize around 40 mm year–1 towards the end of the century and interannual variability will still increase but to a smaller extent. The results emphasize a large uncertainty in the Inet projected by various GCMs.

Modeling reservoir surface temperatures for regional and global climate models: a multi-model study on the inflow and level variation effects

The complexity of the state-of-the-art climate models requires high computational resources and imposes rather simplified parameterization of inland waters. The effect of lakes and reservoirs on the local and regional climate is commonly parameterized in regional or global climate modeling as a function of surface water temperature estimated by atmosphere-coupled one-dimensional lake models. The latter typically neglect one of the major transport mechanisms specific to artificial reservoirs: heat and mass advection due to inflows and outflows. Incorporation of these essentially two-dimensional processes into lake parameterizations requires a trade-off between computational efficiency and physical soundness, which is addressed in this study. We evaluated the performance of the two most used lake parameterization schemes and a machine-learning approach on high-resolution historical water temperature records from 24 reservoirs. Simulations were also performed at both variable and constant water level to explore the thermal structure differences between lakes and reservoirs. Our results highlight the need to include anthropogenic inflow and outflow controls in regional and global climate models. Our findings also highlight the efficiency of the machine-learning approach, which may overperform process-based physical models in both accuracy and computational requirements if applied to reservoirs with long-term observations available. Overall, results suggest that the combined use of process-based physical models and machine-learning models will considerably improve the modeling of air–lake heat and moisture fluxes. A relationship between mean water retention times and the importance of inflows and outflows is established: reservoirs with a retention time shorter than ∼ 100 d, if simulated without inflow and outflow effects, tend to exhibit a statistically significant deviation in the computed surface temperatures regardless of their morphological characteristics.

Km-resolution climate models: prospects and challenges

<p>Currently major efforts are underway toward refining the horizontal grid spacing of climate models to about 1 km, using both global and regional climate models. There is the well-founded hope that this increase in resolution will improve climate models, as it enables replacing the parameterizations of moist convection and gravity-wave drag by explicit treatments. Results suggest that this approach has a high potential in improving the representation of the water cycle and extreme events, and in reducing uncertainties in climate change projections. The presentation will provide examples of these developments in the areas of heavy precipitation and severe weather events over Europe. In addition, it will be argued that km-resolution is a promising approach toward constraining uncertainties in global climate change projections, due to improvements in the representation of tropical and subtropical clouds. Work in the latter area has only recently started and results are highly encouraging.</p> <p>For a few years there have also been attempts to make km-resolution available in global climate models for decade-long simulations. Developing this approach requires a concerted effort. Key challenges include the exploitation of the next generation hardware architectures using accelerators (e.g. graphics processing units, GPUs), the development of suitable approaches to overcome the output avalanche, and the maintenance of the rapidly-developing model source codes on a number of different compute architectures. Despite these challenges, it will be argued that km-resolution GCMs with a capacity to run at 1 SYPD (simulated year per day), might be much closer than commonly believed.</p> <p>The presentation is largely based on a recent collaborative paper (Schär et al., 2020, BAMS, https://doi.org/10.1175/BAMS-D-18-0167.1) and ongoing studies. It will also present aspects of a recent Swiss project in this area (EXCLAIM, https://exclaim.ethz.ch/).</p>

Climate Projections Over Different Climatic Regions of Afghanistan for Shared Socioeconomic Scenarios

The global climate models (GCMs) of Coupled Model Intercomparison Project phase 6 (CMIP6) were used spatiotemporal projections of precipitation and temperature over Afghanistan for three shared socioeconomic pathways (SSP1-2.6, 2-4.5 and 5-8.5) and two future time horizons, early (2020-2059) and late (2060-2099). The Compromise Programming (CP) approach was employed to order the GCMs based on their skill to replicate precipitation and temperature climatology for the reference period (1975-2014). Three models, namely ACCESS-CM2, MPI-ESM1-2-LR, and FIO-ESM-2-0, showed the highest skill in simulating all three variables, and therefore, were chosen for the future projections. The ensemble mean of the GCMs showed an increase in maximum temperature by 1.5-2.5oC, 2.7-4.3 oC, and 4.5-5.3 oC and minimum temperature by 1.3-1.8 oC, 2.2-3.5 oC, and 4.6-5.2 oC for SSP1-2.6, SSP2-4.5, and SSP5-8.5, respectively in the later period. Meanwhile, the changes in precipitation in the range of -15-18%, -36-47% and -40-68% for SSP1-2.6, SSP2-4.5, and SSP5-8.5, respectively. The temperature and precipitation were projected to increase in the highlands and decrease over the deserts, indicating dry regions would be drier and wet regions wetter.

Risk and reward of the global truffle sector under predicted climate change

Climate change has been described as the main threat for the cultivation and growth of truffles, but hydroclimate variability and model uncertainty challenge regional projections and adaptation strategies of the emerging sector. Here, we conduct a literature review to define the main Périgord truffle growing regions around the world and use 20 global climate models to assess the impact of future trends and extremes in temperature, precipitation and soil moisture on truffle production rates and price levels in all cultivation regions in the Americas, Europe, South Africa, and Australasia. Climate model simulations project 2.3 million km2 of suitable land for truffle growth will experience 50% faster aridification than the rests of the global land surface, with significantly more heat waves between 2070 and 2099 CE. Overall, truffle production rates will decrease by ~15%, while associated price levels will increase by ~36%. At the same time, a predicted increase in summer precipitation and less intense warming over Australasia will likely alleviate water scarcity and support higher yields of more affordable truffles. Our findings are relevant for truffle farmers and businesses to adapt their irrigation systems and management strategies to future climate change.

Modelling the Impacts of Climate Change on the Yield of Crops

This study aims to improve the understanding of the impact changes being experienced in our climate system will have on the level of crop productivity in the immediate period as well as in the nearest future. Nigeria was used as a case study and an observed climatic dataset was obtained and used alongside collected 20 year cassava, rice and soybean yield data to develop models that were applied to estimate future crop yield. Four statistically downscaled and bias-corrected Global Climate Models (GCMs): NOAA, MIROC5, ICHEC, and NCC performed simulations for the period 1985–2100 under the Representative Concentration Pathway RCP8.5. These were used to predict how the yields of cassava, rice and soybean will be in the years 2020-2050 and 2070-2100 for the 36 states in Nigeria and the FCT. 89 Empirical models were developed to estimate the yields of the three crops earlier mentioned across Nigeria with their coefficient of determination (R2) ranging between 15% - 99%. The result showed an increase of 3.91% (P<0.001), 0.08, 1.79 (P<0.1) and a decrease of 0.93% for cassava yield for ICHEC, MIROC, NOAA and NCC respectively. It also projected an increase in yield of 8.88% (P<0.001), 7.77% (P<0.001), 6.62% (P<0.001) and 8.85% (P<0.001) for Rice yield using climatic data from ICHEC, MIROC, NOAA and NCC respectively. Soybean, increase in yield are 2.81% (P<0.01), 5.84% (P<0.001), 11.38 (P<0.001) and 9.06% (P<0.001) for ICHEC, MIROC, NOAA and NCC respectively.

Estimating the role of climate internal variability and source of uncertainties in hydrological climate-impact projections

Hydrological climate-impact projections in future are limited by large uncertainties from various sources. Therefore, this study aimed to explore and estimate the sources of uncertainties involved in climate changing impacted assessment in a representative watershed of Northeastern China. Moreover, recent researches indicated that the climate internal variability (CIV) plays an important role in various of hydrological climate-impact projections. Six downscaled Global climate models (GCMs) under two emission scenarios and a calibrate Soil and Water Assessment Tool (SWAT) model were used to obtain hydrological projections in future periods. The CIV and signal-to-noise ratio (SNR) are investigated to analyze the the role of internal variability in hydrological projections. The results shows that the internal variability shows a considerable influence on hydrological projections, which need be partitioned and quantified particularly. Moreover, it worth noting the CIV can propagate from precipitation and ET to runoff projections through the hydrological simulation process. In order to partition the CIV and sources of uncertainties, the uncertainty decomposed frameworks based on analysis of variance (ANOVA) are established. The results demonstrate that the CIV and GCMs are the dominate contributors of runoff in rainy season. In contrast, the CIV and SWAT model parameter sets provided obvious uncertainty to runoff in January to May and October to December. The findings of this study advised that the uncertainty is complex in hydrological simulation process hence, it is meaning and necessary to estimate the uncertainty in climate simulation process, the uncertainty analysis results can provide effectively efforts to reduce uncertainty and then give some positive suggestions to stakeholders for adaption countermeasure under climate change.