Uncertainty In Precipitation Projections Research Articles

A revised interpretation of signal-to-noise ratio paradox and its application to constrain regional climate projections

The signal-to-noise ratio paradox is interpreted as the climate model’s ability to predict observations better than the model itself. This view is counterintuitive, given that climate models are simplified numerical representations of complex earth system dynamics. A revised interpretation is provided here: the signal-to-noise ratio paradox represents excessive noise in climate predictions and projections. Noise is potentially reducible, providing a scientific basis for improving the signal in regional climate projections. The signal-to-noise ratio paradox was assessed in long-term climate projections using single-model and multi-model large ensemble climate data. A null hypothesis was constructed by performing bootstrap resampling of climate model ensembles to test its ability to predict the 20th-century temperature and precipitation trends locally and compare it with the observations. The rejection of the null hypothesis indicates the existence of a paradox. The multi-model large ensemble does not reject the null hypothesis in most places globally. The rejection rate in the single-model large ensemble is related to the model’s fidelity to simulate internal climate variability rather than its ensemble size. For regions where the null hypothesis is rejected in the multi-model large ensemble, for example, India, the paradox is caused by a smaller signal strength in the climate model’s ensemble. The signal strength was improved by 100% through ensemble selection and based on past performance, which reduced uncertainty in India’s 30-year temperature projections by 25%. Consistent with previous studies, precipitation projections are noisier, leading to a paradox metric value 2–3 times higher than that of the temperature projections. The application of ensemble selection methodology significantly decreased uncertainty in precipitation projections for the United Kingdom, Western Australia, and Northeastern America by 47%, 36%, and 20%, respectively. Overall, this study makes a unique contribution by reducing uncertainty at the temporal scale, specifically in estimating trends using the signal-to-noise ratio paradox metric.

Multi-scenario simulation of runoff and nutrient loads in a rapidly urbanizing watershed during China's Dual Carbon periods

Apprehending the hydrological and nutrient variations in rapidly urbanizing watersheds under changing environments is crucial for pollution control and water resource management. However, existing studies have primarily focused on hydrological processes, neglecting water quality aspects, and comprehensive assessment of future runoff and nutrient loads in these watersheds during China's Dual Carbon periods is limited. This study firstly bridges these gaps by constructing multi-scenario with different levels of “Urban Development - Ecological Conservation” and utilizing latest bias-corrected General Circulation Models or Global Climate Models (GCMs) projections to evaluate future runoff and nutrient loads in the Shenzhen River. The calibrated and validated models display satisfactory performance in simulating runoff, nutrient loads, and land use types. The bias-corrected GCMs projections exhibit enhanced accuracy for temperature variables, particularly during the wet season. Implementing effective ecological protection measures is paramount in mitigating water quantity fluctuations and controlling total nitrogen pollution, which is closely associated with urban development and human activities. Conversely, total phosphorus loads demonstrate greater simulation uncertainty, particularly during the dry season of the Carbon Neutrality period, requiring further exploration. Compared to the baseline period, runoff changes minimally, with notable seasonal variations. The findings highlight the escalating uncertainty in load predictions as time progresses. Additionally, addressing uncertainties in precipitation projections driven by GCMs is imperative, given their substantial influence on runoff and nutrient load simulations, particularly during challenging dry seasons. While further research is needed to reduce simulation uncertainty, our study provides valuable insights into nitrogen-phosphorus pollution control and sustainable water resource management in rapidly urbanizing watersheds, especially during the near-term period.

Northward Shifts of the Sahara Desert in Response to Twenty-First-Century Climate Change

Abstract The spatial extent of the Sahara (the largest nonpolar desert) has significant impacts on the livelihood of people residing in its surrounding areas. Despite the fact that climate change would foreseeably impact the location and size of the desert, its future responses (i.e., advance or retreat) are rarely explored in previous studies. Here, through the development of an ensemble Bayesian discriminant analysis approach, we use 10 of the latest high-resolution GCM (global climate model) simulations to document robust annual and seasonal responses of the Sahara Desert to twenty-first-century climate change, with the consideration of modeling uncertainties. We find northward shifts of the Sahara/Sahel and eastern expansion of the nondesert zone under both SSP2–4.5 and SSP5–8.5 scenarios, the former more pronounced in the wet season and the latter in the dry season. Countries located near the Mediterranean may thus experience higher risks of drought, while the projected retreat of the Saharan southern boundary will be beneficial to the local water availability of proximal countries. Significance Statement Given that sub-Saharan Africa is one of the most vulnerable regions to climate change, the Sahara’s expansion would bring unexpected health risks to billions of people. It is thus vital to understand its robust response to global warming. However, previous studies are merely focused on using a simple precipitation threshold as the definition criterion to estimate the varying size of the Sahara Desert. In addition, significant uncertainty in precipitation projections also limits relevant investigations of the Sahara’s future responses. Here, by developing an ensemble Bayesian discriminant analysis approach, we could provide an objective basis for desert identification under large intermodel uncertainty. Further, we find significant northward shifts of both the Sahara and the Sahel, which may induce higher risks of drought over the northwest of North Africa.

ALADIN-Climate at the Hungarian Meteorological Service: from the beginnings to the present day’s results

This study is focusing on the past and, in particular, the present of the ALADIN-Climate model used at the Hungarian Meteorological Service. The currently applied model version is 5.2 (HMS-ALADIN52). In the recent experiments, the CNRM-CM5 global model outputs were downscaled in two steps to 10 km horizontal resolution over Central and Southeast Europe using RCP4.5 and RCP8.5 scenarios. Temperature and precipitation projections are analyzed for 2021-2050 and 2071–2100 with respect to the reference period of 1971–2000 with focus on Hungary. The results are evaluated in comparison to 26 simulations selected from the 12 km horizontal resolution Euro-CORDEX projection ensemble (including two additional versions of ALADIN-Climate: CNRM-ALADIN53 and CNRM-ALADIN63) to get more information about the projection uncertainties over Hungary and to assess the representativeness of HMS-ALADIN52. The HMS-ALADIN52 simulations project a clear warming trend in Central and Southeast Europe, which is more remarkable in case of greater radiative forcing change (RCP8.5). From the 2040s, the Euro-CORDEX simulations start to diverge using different scenarios. The total range of the annual change over Hungary is 1.3–3.3 °C with RCP4.5 and 3.2–5.7 °C with RCP8.5 by the end of the 21st century. HMS-ALADIN52 results are approximately near to the median: 2.9 °C with RCP4.5 and 4 °C with RCP8.5. CNRM-ALADIN53 shows generally similar results to HMS-ALADIN52, but simulations with CNRM-ALADIN63 indicate higher changes compared to both. In terms of seasonal mean precipitation change, the HMS-ALADIN52 simulations assume an increase between 9% and 33% (less in spring, more in autumn) over Hungary in both periods and with both scenarios. Most of the selected Euro-CORDEX simulations show a precipitation increase, apart from summer, when growth and reduction can be equally expected in 2021–2050, and the drying tendency continues towards the end of the century. Increase projected by HMS-ALADIN52 is mostly confirmed by CNRM-ALADIN53, while CNRM-ALADIN63 predicts precipitation decrease in summer. Precipitation results do not show a significantly striking difference between the scenarios, likely due to the fact that internal variability and model uncertainty are more relevant sources of uncertainty in precipitation projections over our region.

Future climate and runoff projections across South Asia from CMIP5 global climate models and hydrological modelling

Study regionThe South Asia. Study focusThis paper presents future climate and runoff projections for the South Asia region under the RCP8.5 scenario with climate change informed by 42 CMIP5 GCMs. Runoff is projected for 0.5° grids using hydrological models with future climate inputs obtained by empirically scaling the historical climate series. New hydrological insightsThe modelling results indicate that future runoff will increase throughout most of the region except in the far north-east and far north-west. The median projection shows increases in mean annual runoff of 20–30% in the Indian sub-continent for 2046–2075 relative to 1976–2005. The change in runoff is driven mainly by the change in precipitation, moderated (in wetter futures) or intensified (in drier futures) by higher temperature and potential evaporation. The paper also investigates the uncertainties of the projection due to scaling methods and selection of GCMs. The difference in runoff projections from different scaling methods is small relative to the large uncertainty in precipitation projections from the GCMs. Sub-selecting only the “better” performing GCMs shows marginal difference in the uncertainty range of projected runoff. For the broad scale projections presented here, it is best to use projections informed by all the GCMs to provide an indication of the full uncertainty range.

Greenhouse gas scenario sensitivity and uncertainties in precipitation projections for central Belgium

Climate change impact assessment on meteorological variables involves large uncertainties as a result of incomplete knowledge on the future greenhouse gas concentrations and climate model physics, next to the inherent internal variability of the climate system. Given that the alteration in greenhouse gas concentrations is the driver for the change, one expects the impacts to be highly dependent on the considered greenhouse gas scenario (GHS). In this study, we denote this behavior as GHS sensitivity. Due to the climate model related uncertainties, this sensitivity is, at local scale, not always that strong as expected. This paper aims to study the GHS sensitivity and its contributing role to climate scenarios for a case study in Belgium. An ensemble of 160 CMIP5 climate model runs is considered and climate change signals are studied for precipitation accumulation, daily precipitation intensities and wet day frequencies. This was done for the different seasons of the year and the scenario periods 2011–2040, 2031–2060, 2051–2081 and 2071–2100. By means of variance decomposition, the total variance in the climate change signals was separated in the contribution of the differences in GHSs and the other model-related uncertainty sources. These contributions were found dependent on the variable and season. Following the time of emergence concept, the GHS uncertainty contribution is found dependent on the time horizon and increases over time. For the most distinct time horizon (2071–2100), the climate model uncertainty accounts for the largest uncertainty contribution. The GHS differences explain up to 18% of the total variance in the climate change signals. The results point further at the importance of the climate model ensemble design, specifically the ensemble size and the combination of climate models, whereupon climate scenarios are based.The numerical noise, introduced at scales smaller than the skillful scale, e.g. at local scale, was not considered in this study.

Multimodel assessment of sensitivity and uncertainty of evapotranspiration and a proxy for available water resources under climate change

Partitioning of precipitation (P) into actual evapotranspiration (ET) and runoff affects a proxy for water availability (P-ET) on land surface. ET accounts for more than 60% of global precipitation and affects both water and energy cycles. We study the changes in precipitation, air temperature, ET, and P-ET in seven large basins under the RCP 2.6 and 8.5 scenarios for the projected future climate. While a majority of studied basins is projected to experience a warmer and wetter climate, uncertainty in precipitation projections remains large in comparison to the temperature projections. Due to high uncertainty in ET, uncertainties in fraction of precipitation that is evaporated (ET/P) and a proxy for available water (P-ET) are also large under the projected future climate. Our assessment showed that under the RCP 8.5 scenario, global climate models are major contributors to uncertainties in ET (P-ET) simulations in the four (six) basins, while uncertainty due to hydrological models is prevailing or comparable in the other three (one) basins. The simulated ET is projected to increase under the warmer and wetter future climates in all the basins and periods under both RCPs. Regarding P-ET, it is projected to increase in five out of seven basins in the End term (2071–2099) under the RCP 8.5 scenario. Precipitation elasticity and temperature sensitivity estimated for ET were found to be positive in all the basins under the RCP 8.5 scenario. In contrast, the temperature sensitivity estimated for (P-ET) was found to be negative for all the basins under the RCP 8.5 scenario, indicating the role of increased energy availability and limited soil moisture. Our results highlight the need for improvements in climate and hydrological models with better representation of soil, vegetation, and cold season processes to reduce uncertainties in the projected ET and P-ET.

Vegetation plays an important role in mediating future water resources

Future environmental change is expected to modify the global hydrological cycle, with consequences for the regional distribution of freshwater supplies. Regional precipitation projections, however, differ largely between models, making future water resource projections highly uncertain. Using two representative concentration pathways and nine climate models, we estimate 21st century water resources across Australia, employing both a process-based dynamic vegetation model and a simple hydrological framework commonly used in water resource studies to separate the effects of climate and vegetation on water resources. We show surprisingly robust, pathway-independent regional patterns of change in water resources despite large uncertainties in precipitation projections. Increasing plant water use efficiency (due to the changing atmospheric CO2) and reduced green vegetation cover (due to the changing climate) relieve pressure on water resources for the highly populated, humid coastal regions of eastern Australia. By contrast, in semi-arid regions across Australia, runoff declines are amplified by CO2-induced greening, which leads to increased vegetation water use. These findings highlight the importance of including vegetation dynamics in future water resource projections.

Evaluation of historical and future simulations of precipitation and temperature in central Africa from CMIP5 climate models

AbstractGlobal and regional climate change assessments rely heavily on the general circulation model (GCM) outputs such as provided by the Coupled Model Intercomparison Project phase 5 (CMIP5). Here we evaluate the ability of 25 CMIP5 GCMs to simulate historical precipitation and temperature over central Africa and assess their future projections in the context of historical performance and intermodel and future emission scenario uncertainties. We then apply a statistical bias correction technique to the monthly climate fields and develop monthly downscaled fields for the period of 1948–2099. The bias‐corrected and downscaled data set is constructed by combining a suite of global observation and reanalysis‐based data sets, with the monthly GCM outputs for the 20th century, and 21st century projections for the medium mitigation (representative concentration pathway (RCP)45) and high emission (RCP85) scenarios. Overall, the CMIP5 models simulate temperature better than precipitation, but substantial spatial heterogeneity exists. Many models show limited skill in simulating the seasonality, spatial patterns, and magnitude of precipitation. Temperature projections by the end of the 21st century (2070–2099) show a robust warming between 2 and 4°C across models, whereas precipitation projections vary across models in the sign and magnitude of change (−9% to 27%). Projected increase in precipitation for a subset of models (single model ensemble (SME)) identified based on performance metrics and causal mechanisms are slightly higher compared to the full multimodel ensemble (MME) mean; however, temperature projections are similar between the two ensemble means. For the near‐term (2021–2050), neither the historical performance nor choice of models is related to the precipitation projections, indicating that natural variability dominated any signal. With fewer models, the “blind” MME approach will have larger uncertainties in future precipitation projections compared to projections by the SME models. We propose the latter a better approach in regions that lack quality climate observations. Our analyses also show that the choice of model and emission scenario dominate the uncertainty in precipitation projections, whereas the emission scenario dominates the temperature projections. Although our analyses are done for central Africa, the final Bias‐Corrected Spatially Downscaled data set is available for global land areas. The framework for climate change assessment and the data will be useful for a variety of climate assessment, impact, and adaptation studies.

Projections of climate change impacts on potential C4 crop productivity over tropical regions

Climate change impacts on agriculture could arguably be most critical for developing countries in tropical regions: their populations rely importantly on agriculture and climate-dependant resources, poverty limits their capacity to anticipate and adapt to climate change, and population increase already poses a serious challenge to food security in those regions. Current projections of climate change impacts on tropical crop yields, even though on average negative, remain largely uncertain: there is need for more consistent, large-scale, quantitative assessments.In this study we use a newly developed agro-DGVM (Dynamical Global Vegetation Model including an explicit representation of croplands) driven by projections from several climate models and two SRES scenarios to evaluate climate change impacts on potential C4 crop productivity over Africa and India from 1960 to 2100. We specifically separate the effect of increasing atmospheric CO2 levels. We perform transient simulations directly forced by climate model outputs: to preserve consistency in the analysis despite regional biases in climate models, we analyze yield change on a bioclimatic basis (using the Köppen classification) rather than on a geographical basis. We find that the potential productivity of one of the most important staple crops in those regions, millet, will overall decrease, on average over all models and scenarios, by −6% (individual model projections ranging from −29% to +11%). The bioclimatic analysis allows us to highlight the main climate drivers of these changes. The main impact is a moderate but robust temperature-driven yield decrease over Equatorial and Temperate Köppen zones; larger but much more inconsistent yield changes occur in Arid Köppen zones, reflecting the uncertainty in precipitation projections from climate models. The uncertainty in aggregated impacts reflects the uncertainty over these areas, underlining the need to narrow the uncertainty in precipitation projections over dry areas if more reliable agricultural impact assessments over tropical regions are to be provided. Our results are also consistent with the limited magnitude of the impact of increased atmospheric CO2 levels on C4 crop yields described in the literature. While such climatic impacts further increase the challenge of achieving future food security in developing countries in the Tropics, most of these impacts can arguably be mitigated through adaptation measures and improved agricultural practices.