Historical and future trends in wetting and drying in 291 catchments across China

Abstract. The "dry gets drier, wet gets wetter" (DDWW) pattern is a popular catchphrase to summarize hydrologic changes under global warming. However, recent studies based on simulated data have failed to obtain a feasible DDWW pattern for runoff trends. This study tested the DDWW pattern using observed streamflow and meteorological data from 291 catchments in China from 1956 to 2000, interpreted it using a simple method derived from the Budyko hypothesis, and explored its future evolution according to the projections of five global climate models (GCMs) from the Coupled Model Intercomparison Project Phase 5 (CMIP5). Similar to the DDWW pattern, the results show that catchments with an aridity index of φ 1 become drier, with nearly 80 % of the studied catchments following this pattern. However, the pattern does not hold in glacier regions due to the effects of melting ice and snow. Based on precipitation and potential evapotranspiration changes, the first-order differential of the Budyko hypothesis can provide a good estimate of runoff changes (R2 = 0.70). Therefore, the atmospheric forcing of water and energy is the key factor in interpreting the DDWW pattern. Over 80 % of the estimated trends have signs coincident with those of the measured trends, implying that the DDWW pattern can be assessed with estimated data. Precipitation is the controlling factor that leads to the DDWW pattern in nearly 90 % of catchments where observed and estimated signs are consistent. In the three tested scenarios (RCP2.6, RCP4.5 and RCP8.5), the different models produce significantly different predicted changes, even under the same scenario, whereas a given model yields similar results under different scenarios. Based on the projected results, the DDWW pattern no longer provides a reliable prediction. However, this conclusion remains tentative due to the large uncertainty of the simulations. The considerable differences between the observed and modelled meteorological data for the same period suggest that this conclusion should be adopted with caution.

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Terrestrial Aridity and Its Response to Greenhouse Warming across CMIP5 Climate Models

Journal of Climate ◽

10.1175/jcli-d-14-00480.1 ◽

2015 ◽

Vol 28 (14) ◽

pp. 5583-5600 ◽

Cited By ~ 76

Author(s):

Jacob Scheff ◽

Dargan M. W. Frierson

Keyword(s):

Land Surface ◽

Large Scale ◽

Climate Models ◽

Potential Evapotranspiration ◽

Global Climate ◽

Aridity Index ◽

Global Climate Models ◽

Greenhouse Warming ◽

Dimensionless Ratio ◽

The Tropics

Abstract The aridity of a terrestrial climate is often quantified using the dimensionless ratio of annual precipitation (P) to annual potential evapotranspiration (PET). In this study, the climatological patterns and greenhouse warming responses of terrestrial P, Penman–Monteith PET, and are compared among 16 modern global climate models. The large-scale climatological values and implied biome types often disagree widely among models, with large systematic differences from observational estimates. In addition, the PET climatologies often differ by several tens of percent when computed using monthly versus 3-hourly inputs. With greenhouse warming, land P does not systematically increase or decrease, except at high latitudes. Therefore, because of moderate, ubiquitous PET increases, decreases (drying) are much more widespread than increases (wetting) in the tropics, subtropics, and midlatitudes in most models, confirming and expanding on earlier findings. The PET increases are also somewhat sensitive to the time resolution of the inputs, although not as systematically as for the PET climatologies. The changes in the balance between P and PET are also quantified using an alternative aridity index, the ratio , which has a one-to-one but nonlinear correspondence with . It is argued that the magnitudes of changes are more uniformly relevant than the magnitudes of changes, which tend to be much higher in wetter regions. The ratio and its changes are also found to be excellent statistical predictors of the land surface evaporative fraction and its changes.

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Historical and future trends in wetting and drying in 291 catchments across China

Hydrology and Earth System Sciences ◽

10.5194/hess-21-2233-2017 ◽

2017 ◽

Vol 21 (4) ◽

pp. 2233-2248 ◽

Cited By ~ 11

Author(s):

Zhongwang Chen ◽

Huimin Lei ◽

Hanbo Yang ◽

Dawen Yang ◽

Yongqiang Cao

Keyword(s):

Climate Changes ◽

Climate Models ◽

Potential Evapotranspiration ◽

Global Climate ◽

Coupled Model ◽

Global Climate Models ◽

Future Trends ◽

Wetting And Drying ◽

Intercomparison Project ◽

Streamflow Data

Abstract. An increasingly uneven distribution of hydrometeorological factors related to climate change has been detected by global climate models (GCMs) in which the pattern of changes in water availability is commonly described by the phrase dry gets drier, wet gets wetter (DDWW). However, the DDWW pattern is dominated by oceanic areas; recent studies based on both observed and modelled data have failed to verify the DDWW pattern on land. This study confirms the existence of a new DDWW pattern in China after analysing the observed streamflow data from 291 Chinese catchments from 1956 to 2000, which reveal that the distribution of water resources has become increasingly uneven since the 1950s. This pattern can be more accurately described as drier regions are more likely to become drier, whereas wetter regions are more likely to become wetter. Based on a framework derived from the Budyko hypothesis, this study estimates runoff trends via observations of precipitation (P) and potential evapotranspiration (Ep) and predicts the future trends from 2001 to 2050 according to the projections of five GCMs from the Coupled Model Intercomparison Project Phase 5 (CMIP5) under three scenarios: RCP2.6, RCP4.5, and RCP8.5. The results show that this framework has a good performance for estimating runoff trends; such changes in P play the most significant role. Most areas of China, including more than 60 % of catchments, will experience water resource shortages under the projected climate changes. Despite the differences among the predicted results of the different models, the DDWW pattern does not hold in the projections regardless of the model used. Nevertheless, this conclusion remains tentative owing to the large uncertainties in the GCM outputs.

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Observation-based selection of climate models projects Arctic ice-free summers around 2035

Communications Earth & Environment ◽

10.1038/s43247-021-00214-7 ◽

2021 ◽

Vol 2 (1) ◽

Author(s):

David Docquier ◽

Torben Koenigk

Keyword(s):

Model Selection ◽

Sea Ice ◽

Climate Models ◽

Global Climate ◽

Coupled Model ◽

Arctic Sea Ice ◽

Global Climate Models ◽

The Arctic ◽

Arctic Sea ◽

Area And Volume

AbstractArctic sea ice has been retreating at an accelerating pace over the past decades. Model projections show that the Arctic Ocean could be almost ice free in summer by the middle of this century. However, the uncertainties related to these projections are relatively large. Here we use 33 global climate models from the Coupled Model Intercomparison Project 6 (CMIP6) and select models that best capture the observed Arctic sea-ice area and volume and northward ocean heat transport to refine model projections of Arctic sea ice. This model selection leads to lower Arctic sea-ice area and volume relative to the multi-model mean without model selection and summer ice-free conditions could occur as early as around 2035. These results highlight a potential underestimation of future Arctic sea-ice loss when including all CMIP6 models.

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Tendency Bias Correction in Coupled and Uncoupled Global Climate Models with a Focus on Impacts over North America

Journal of Climate ◽

10.1175/jcli-d-18-0598.1 ◽

2019 ◽

Vol 32 (2) ◽

pp. 639-661 ◽

Cited By ~ 5

Author(s):

Y. Chang ◽

S. D. Schubert ◽

R. D. Koster ◽

A. M. Molod ◽

H. Wang

Keyword(s):

North America ◽

Bias Correction ◽

Climate Models ◽

Global Climate ◽

Storm Track ◽

Coupled Model ◽

Global Climate Models ◽

Forecast Skill ◽

Boreal Summer ◽

Transient Wave

Abstract We revisit the bias correction problem in current climate models, taking advantage of state-of-the-art atmospheric reanalysis data and new data assimilation tools that simplify the estimation of short-term (6 hourly) atmospheric tendency errors. The focus is on the extent to which correcting biases in atmospheric tendencies improves the model’s climatology, variability, and ultimately forecast skill at subseasonal and seasonal time scales. Results are presented for the NASA GMAO GEOS model in both uncoupled (atmosphere only) and coupled (atmosphere–ocean) modes. For the uncoupled model, the focus is on correcting a stunted North Pacific jet and a dry bias over the central United States during boreal summer—long-standing errors that are indeed common to many current AGCMs. The results show that the tendency bias correction (TBC) eliminates the jet bias and substantially increases the precipitation over the Great Plains. These changes are accompanied by much improved (increased) storm-track activity throughout the northern midlatitudes. For the coupled model, the atmospheric TBCs produce substantial improvements in the simulated mean climate and its variability, including a much reduced SST warm bias, more realistic ENSO-related SST variability and teleconnections, and much improved subtropical jets and related submonthly transient wave activity. Despite these improvements, the improvement in subseasonal and seasonal forecast skill over North America is only modest at best. The reasons for this, which are presumably relevant to any forecast system, involve the competing influences of predictability loss with time and the time it takes for climate drift to first have a significant impact on forecast skill.

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Robust increase of Indian monsoon rainfall and its variability under future warming in CMIP-6 models

10.5194/esd-2020-80 ◽

2020 ◽

Author(s):

Anja Katzenberger ◽

Jacob Schewe ◽

Julia Pongratz ◽

Anders Levermann

Keyword(s):

Climate Change ◽

Global Warming ◽

Climate Models ◽

Monsoon Rainfall ◽

Global Climate ◽

Coupled Model ◽

Global Climate Models ◽

Future Climate Change ◽

Global Mean Temperature ◽

Almost All

Abstract. The Indian summer monsoon is an integral part of the global climate system. As its seasonal rainfall plays a crucial role in India's agriculture and shapes many other aspects of life, it affects the livelihood of a fifth of the world's population. It is therefore highly relevant to assess its change under potential future climate change. Global climate models within the Coupled Model Intercomparison Project Phase 5 (CMIP-5) indicated a consistent increase in monsoon rainfall and its variability under global warming. Since the range of the results of CMIP-5 was still large and the confidence in the models was limited due to partly poor representation of observed rainfall, the updates within the latest generation of climate models in CMIP-6 are of interest. Here, we analyse 32 models of the latest CMIP-6 exercise with regard to their annual mean monsoon rainfall and its variability. All of these models show a substantial increase in June-to-September (JJAS) mean rainfall under unabated climate change (SSP5-8.5) and most do also for the other three Shared Socioeconomic Pathways analyzed (SSP1-2.6, SSP2-4.5, SSP3-7.0). Moreover, the simulation ensemble indicates a linear dependence of rainfall on global mean temperature with high agreement between the models and independent of the SSP; the multi-model mean for JJAS projects an increase of 0.33 mm/day and 5.3 % per degree of global warming. This is significantly higher than in the CMIP-5 projections. Most models project that the increase will contribute to the precipitation especially in the Himalaya region and to the northeast of the Bay of Bengal, as well as the west coast of India. Interannual variability is found to be increasing in the higher-warming scenarios by almost all models. The CMIP-6 simulations largely confirm the findings from CMIP-5 models, but show an increased robustness across models with reduced uncertainties and updated magnitudes towards a stronger increase in monsoon rainfall.

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A simple approach to mimic the effect of active vegetation in hydrological models to better estimate hydrological variables under climate change

10.5194/egusphere-egu21-12025 ◽

2021 ◽

Author(s):

Thedini Asali Peiris ◽

Petra Döll

Keyword(s):

Climate Change ◽

Land Surface ◽

Climate Models ◽

Potential Evapotranspiration ◽

Global Climate ◽

Global Climate Models ◽

Hydrological Models ◽

Net Radiation ◽

Ensemble Mean ◽

The Impact

Unlike global climate models, hydrological models cannot simulate the feedbacks among atmospheric processes, vegetation, water, and energy exchange at the land surface. This severely limits their ability to quantify the impact of climate change and the concurrent increase of atmospheric CO2 concentrations on evapotranspiration and thus runoff. Hydrological models generally calculate actual evapotranspiration as a fraction of potential evapotranspiration (PET), which is computed as a function of temperature and net radiation and sometimes of humidity and wind speed. Almost no hydrological model takes into account that PET changes because the vegetation responds to changing CO2 and climate. This active vegetation response consists of three components. With higher CO2 concentrations, 1) plant stomata close, reducing transpiration (physiological effect) and 2) plants may grow better, with more leaves, increasing transpiration (structural effect), while 3) climatic changes lead to changes in plants growth and even biome shifts, changing evapotranspiration. Global climate models, which include dynamic vegetation models, simulate all these processes, albeit with a high uncertainty, and take into account the feedbacks to the atmosphere.Milly and Dunne (2016) (MD) found that in the case of RCP8.5 the change of PET (computed using the Penman-Monteith equation) between 1981- 2000 and 2081-2100 is much higher than the change of non-water-stressed evapotranspiration (NWSET) computed by an ensemble of global climate models. This overestimation is partially due to the neglect of active vegetation response and partially due to the neglected feedbacks between the atmosphere and the land surface.The objective of this paper is to present a simple approach for hydrological models that enables them to mimic the effect of active vegetation on potential evapotranspiration under climate change, thus improving computation of freshwater-related climate change hazards by hydrological models. MD proposed an alternative approach to estimate changes in PET for impact studies that is only a function of the changes in energy and not of temperature and achieves a good fit to the ensemble mean change of evapotranspiration computed by the ensemble of global climate models in months and grid cells without water stress. We developed an implementation of the MD idea for hydrological models using the Priestley-Taylor equation (PET-PT) to estimate PET as a function of net radiation and temperature. With PET-PT, an increasing temperature trend leads to strong increases in PET. Our proposed methodology (PET-MD) helps to remove this effect, retaining the impact of temperature on PET but not on long-term PET change.We implemented the PET-MD approach in the global hydrological model WaterGAP2.2d. and computed daily time series of PET between 1981 and 2099 using bias-adjusted climate data of four global climate models for RCP 8.5. We evaluated, computed PET-PT and PET-MD at the grid cell level and globally, comparing also to the results of the Milly-Dunne study. The global analysis suggests that the application of PET-MD reduces the PET change until the end of this century from 3.341 mm/day according to PET-PT to 3.087 mm/day (ensemble mean over the four global climate models).Milly, P.C.D., Dunne K.A. (2016). DOI:10.1038/nclimate3046.

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Evaluation of CMIP5 Global Climate Models for Simulating Climatological Temperature and Precipitation for Southeast Asia

Advances in Meteorology ◽

10.1155/2019/1067365 ◽

2019 ◽

Vol 2019 ◽

pp. 1-18 ◽

Cited By ~ 9

Author(s):

Suchada Kamworapan ◽

Chinnawat Surussavadee

Keyword(s):

Southeast Asia ◽

Climate Models ◽

Performance Metrics ◽

Global Climate ◽

Total Error ◽

Coupled Model ◽

Global Climate Models ◽

Ensemble Average ◽

Temperature And Precipitation ◽

Intercomparison Project

This study evaluates the performances of all forty different global climate models (GCMs) that participate in the Coupled Model Intercomparison Project Phase 5 (CMIP5) for simulating climatological temperature and precipitation for Southeast Asia. Historical simulations of climatological temperature and precipitation of the 40 GCMs for the 40-year period of 1960–1999 for both land and sea and those for the century of 1901–1999 for land are evaluated using observation and reanalysis datasets. Nineteen different performance metrics are employed. The results show that the performances of different GCMs vary greatly. CNRM-CM5-2 performs best among the 40 GCMs, where its total error is 3.25 times less than that of GCM performing worst. The performance of CNRM-CM5-2 is compared with those of the ensemble average of all 40 GCMs (40-GCM-Ensemble) and the ensemble average of the 6 best GCMs (6-GCM-Ensemble) for four categories, i.e., temperature only, precipitation only, land only, and sea only. While 40-GCM-Ensemble performs best for temperature, 6-GCM-Ensemble performs best for precipitation. 6-GCM-Ensemble performs best for temperature and precipitation simulations over sea, whereas CNRM-CM5-2 performs best over land. Overall results show that 6-GCM-Ensemble performs best and is followed by CNRM-CM5-2 and 40-GCM-Ensemble, respectively. The total errors of 6-GCM-Ensemble, CNRM-CM5-2, and 40-GCM-Ensemble are 11.84, 13.69, and 14.09, respectively. 6-GCM-Ensemble and CNRM-CM5-2 agree well with observations and can provide useful climate simulations for Southeast Asia. This suggests the use of 6-GCM-Ensemble and CNRM-CM5-2 for climate studies and projections for Southeast Asia.

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Hydroclimatic Extremes in the Limpopo River Basin, South Africa, under Changing Climate

Water ◽

10.3390/w12123299 ◽

2020 ◽

Vol 12 (12) ◽

pp. 3299

Author(s):

Christina M. Botai ◽

Joel O. Botai ◽

Nosipho N. Zwane ◽

Patrick Hayombe ◽

Eric K. Wamiti ◽

...

Keyword(s):

South Africa ◽

River Basin ◽

Climate Models ◽

Research Study ◽

Global Climate ◽

Regional Climate ◽

Coupled Model ◽

Global Climate Models ◽

Trend Test ◽

Changing Climate

This research study evaluated the projected future climate and anticipated impacts on water-linked sectors on the transboundary Limpopo River Basin (LRB) with a focus on South Africa. Streamflow was simulated from two CORDEX-Africa regional climate models (RCMs) forced by the 5th phase of the Coupled Model Inter-Comparison Project (CMIP5) Global Climate Models (GCMs), namely, the CanESM2m and IPSL-CM5A-MR climate models. Three climate projection time intervals were considered spanning from 2006 to 2099 and delineated as follows: current climatology (2006–2035), near future (2036–2065) and end of century future projection (2070–2099). Statistical metrics derived from the projected streamflow were used to assess the impacts of the changing climate on water-linked sectors. These metrics included streamflow trends, low and high flow quantile probabilities, the Standardized Streamflow Index (SSI) trends and the proportion (%) of dry and wet years, as well as drought monitoring indicators. Based on the Mann-Kendall (MK) trend test, the LRB is projected to experience reduced streamflow in both the near and the distant future. The basin is projected to experience frequent dry and wet conditions that can translate to drought and flash floods, respectively. In particular, a high proportion of dry and a few incidences of wet years are expected in the basin in the future. In general, the findings of this research study will inform and enhance climate change adaptation and mitigation policy decisions and implementation thereof, to sustain the livelihoods of vulnerable communities.

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Projected increases in magnitude and socioeconomic exposure of global droughts in 1.5 and 2 °C warmer climates

Hydrology and Earth System Sciences ◽

10.5194/hess-24-451-2020 ◽

2020 ◽

Vol 24 (1) ◽

pp. 451-472 ◽

Cited By ~ 9

Author(s):

Lei Gu ◽

Jie Chen ◽

Jiabo Yin ◽

Sylvia C. Sullivan ◽

Hui-Min Wang ◽

...

Keyword(s):

Global Warming ◽

Climate Models ◽

Global Climate ◽

Coupled Model ◽

Global Climate Models ◽

Drought Severity ◽

Copula Functions ◽

Infrastructure Failure ◽

Potential Risks ◽

Socioeconomic Consequences

Abstract. The Paris Agreement sets a long-term temperature goal to hold global warming to well below 2.0 ∘C and strives to limit it to 1.5 ∘C above preindustrial levels. Droughts with either intense severity or a long persistence could both lead to substantial impacts such as infrastructure failure and ecosystem vulnerability, and they are projected to occur more frequently and trigger intensified socioeconomic consequences with global warming. However, existing assessments targeting global droughts under 1.5 and 2.0 ∘C warming levels usually neglect the multifaceted nature of droughts and might underestimate potential risks. This study, within a bivariate framework, quantifies the change in global drought conditions and corresponding socioeconomic exposures for additional 1.5 and 2.0 ∘C warming trajectories. The drought characteristics are identified using the Standardized Precipitation Evapotranspiration Index (SPEI) combined with the run theory, with the climate scenarios projected by 13 Coupled Model Inter-comparison Project Phase 5 (CMIP5) global climate models (GCMs) under three representative concentration pathways (RCP 2.6, RCP4.5 and RCP8.5). The copula functions and the most likely realization are incorporated to model the joint distribution of drought severity and duration, and changes in the bivariate return period with global warming are evaluated. Finally, the drought exposures of populations and regional gross domestic product (GDP) under different shared socioeconomic pathways (SSPs) are investigated globally. The results show that within the bivariate framework, the historical 50-year droughts may double across 58 % of global landmasses in a 1.5 ∘C warmer world, while when the warming climbs up to 2.0 ∘C, an additional 9 % of world landmasses would be exposed to such catastrophic drought deteriorations. More than 75 (73) countries' populations (GDP) will be completely affected by increasing drought risks under the 1.5 ∘C warming, while an extra 0.5 ∘C warming will further lead to an additional 17 countries suffering from a nearly unbearable situation. Our results demonstrate that limiting global warming to 1.5 ∘C, compared with 2 ∘C warming, can perceptibly mitigate the drought impacts over major regions of the world.

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The Double-ITCZ Bias in CMIP6 Models

10.5194/egusphere-egu2020-21375 ◽

2020 ◽

Author(s):

Baijun Tian

Keyword(s):

Climate Models ◽

Global Climate ◽

Coupled Model ◽

Global Climate Models ◽

Cmip5 Models ◽

Tropical Precipitation ◽

Double Itcz ◽

Intercomparison Project ◽

Fully Coupled

The double-Intertropical Convergence Zone (ITCZ) bias is one of the most outstanding problems in climate models. This study seeks to examine the double-ITCZ bias in the latest state-of-the-art fully coupled global climate models that participated in Coupled Model Intercomparison Project (CMIP) Phase 6 (CMIP6) in comparison to their previous generations (CMIP3 and CMIP5 models). To that end, we have analyzed the long-term annual mean tropical precipitation distributions and several precipitation bias indices that quantify the double-ITCZ biases in 75 climate models including 24 CMIP3 models, 25 CMIP3 models, and 26 CMIP6 models. We find that the double-ITCZ bias and its big inter-model spread persist in CMIP6 models but the double-ITCZ bias is slightly reduced from CMIP3 or CMIP5 models to CMIP6 models.

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