High-resolution Climate Model Research Articles

A gauge-based sub-daily extreme rainfall climatology for western Europe

We present here the first climatology of extreme sub-daily rainfall for a large part of western Europe, developed using a newly developed set of sub-daily rainfall indices, derived from a recently created global sub-daily rainfall (GSDR) dataset. The indices describe sub-daily (3-hourly) rainfall extremes in terms of their intensity, frequency and timing. Analysis of the frequency index (number of 3hr periods with >20 mm rain (R3hr20mm)) and a suite of intensity indices including the annual maxima (Rx3hr) and values of the 99.9th percentile at each gauge (R99.9p3hr) indicate a peak in the frequency and intensity of short-duration rainfall extremes in summer across most of western Europe, while areas around the north-west Mediterranean experience the highest intensities and frequencies of 3-hr rainfall in autumn. The index of contribution from 3-h annual maxima to the daily total (Rx3hrP) indicates that these events are often produced by very short-duration or very peaked storms, with a tendency towards a late afternoon or evening peak in the time of occurrence. There are also clear differences in the spatiotemporal occurrence of the sub-daily extremes when compared to extreme daily rainfall indices, which could have repercussions for flood management. Additional analysis of the sub-daily rainfall indices within the context of Köppen-Geiger climate zones indicates Mediterranean-type climate zones experience more intense and more frequent sub-daily extremes, while the intensity of rainfall within cooler climate zones is lower and the most intense events are restricted to summer. The climate zones are found to be a relatively good indicator of the extreme rainfall characteristics that can be expected in a region. Being able to compare sub-daily rainfall characteristics across a wide region in this manner greatly enhances our ability to investigate future variability and change in sub-daily extremes and will aid in high-resolution climate model validation.

Impacts of Climate Change on Extreme Climate Indices in Türkiye Driven by High-Resolution Downscaled CMIP6 Climate Models

In this study, the latest release of all available Coupled Model Intercomparison Project Phase 6 (CMIP6) climate models with two future scenarios of Shared Socio-Economic Pathways, SSP2-4.5 and SSP5-8.5, over the period 2015–2100 are utilized in diagnosing climate extremes in Türkiye. Coarse-resolution climate models were downscaled to a 0.1° × 0.1° (~9 km) spatial resolution using the European Centre for Medium-Range Weather Forecasts Reanalysis 5-Land (ERA5-Land) dataset based on three types of quantile mapping: quantile mapping, detrended quantile mapping, and quantile delta mapping. The temporal variations of the 12 extreme precipitation indices (EPIs) and 12 extreme temperature indices (ETIs) from 2015 to 2100 consistently suggest drier conditions, in addition to more frequent and severe precipitation extremes and warming temperature extremes in Türkiye, under the two future scenarios. The SSP5-8.5 scenario indicates more severe water stress than the SSP2-4.5 scenario; the total precipitation decreases up to 20% for Aegean and Mediterranean regions of Türkiye. Precipitation extremes indicate a decrease in the frequency of heavy rains but an increase in very heavy rains and also an increasing amount of the total precipitation from very heavy rain days. Temperature extremes such as the coldest, warmest, and mean daily maximum temperature are expected to increase across all regions of Türkiye, indicating warming conditions by up to 7.5 °C by the end of the century. Additionally, the coldest daily maximums also exhibit higher variability to climate change in the subregions Aegean, Southeastern Anatolia, Marmara, and Mediterranean regions of Türkiye while the mean daily maximum temperature showed greater sensitivity in the Black Sea, Central Anatolia, and Eastern Anatolia regions.

Heterogeneous acceleration algorithms for shallow cumulus convection scheme over GPU clusters

The physical process of atmospheric cumulus convection plays a crucial role in climate modeling, and its complex computational process severely restricts the development of high-resolution climate models. Accelerating the cumulus convective process calculation in climate models is a significant challenge. Traditional CPU-accelerated computing is increasingly unable to meet the growing demand for computing resources from high-resolution climate models. Therefore, developing an efficient cumulus convection scheme is quite valuable and necessary. In response to this demand, this paper selects the University of Washington shallow cumulus (UWshcu) model as the research object and proposes its parallel algorithms suitable for large-scale, heterogeneous, high-performance computing systems: (1) the single GPU acceleration algorithm based on CUDA C, namely GPU-UWshcu; (2) the multi-NVIDIA GPUs acceleration algorithm based on the MPI+CUDA hybrid programming model, namely CGPUs-UWshcu; (3) the multi-AMD GPUs acceleration algorithm based on MPI+HIP, namely HGPUs-UWshcu; (4) the multiple CPUs+GPUs acceleration algorithm based on MPI+OpenMP+HIP, namely MOH-UWshcu. Experimental results show that these algorithms are efficient and have good scalability. GPU-UWshcu achieves a speedup of 74.39× on a single Tesla V100 GPU compared with the serial algorithm running on an Intel Xeon E5-2680 v2 CPU core, and CGPUs-UWshcu achieves a 151.22× speedup on 16 Tesla V100 GPUs compared to a single Intel Xeon E5-2630 v4 CPU (10 cores). On the ORISE supercomputer, HGPUs-UWshcu uses 1024 AMD GPUs to achieve a 664.65× speedup compared to a single CPU (32 cores), with a parallel efficiency of 68.91% compared to using 32 GPUs. Compared to using the same number of CPU cores, MOH-UWshcu uses 8192 CPU cores+1024 GPUs to achieve a speedup of 4.98×, with 55.22 TFLOPS in double precision.

Statistical Assessment of High-Resolution Climate Model Rainfall Data in the Ciliwung Watershed, Indonesia

The impact of climate change on hydrometeorological hazards pointed out the necessity for information on rainfall data. Using Climate Hazard Group InfraRed Precipitation with Station (CHIRPS) data could solve the problem of the scarcity of observed rainfall data at a finer spatial resolution. This paper examines the performance of high-resolution rainfall climate model data called CORDEX SEA and NEXGDPP in the Ciliwung watershed, Indonesia. We used CHIRPS data as observed data, which was separately divided for calibration (1981-2005) and validation (2006-2020) of the climate models. Totally 14 climate models were used, comprised of 4 CORDEX and 10 NEXGDPP. The models accuracy was assessed based on three statistical indicators: bias, mean absolute percentage error (MAPE), and mean square error (MSE). We determined the best model based on Taylor Diagram. The results showed that the bias value in the dry season was smaller than in the wet and transitional seasons. All models performed well as shown by the low bias values except for the ACCESS1-0 RCP8.5 model. The findings revealed that MRI-CGCM was the best model for calibration, whereas EC-Earth was the best model in the validation period for both RCP4.5 and RCP8.5 scenarios. Further, the choice of climate model may influence water resource management over watershed scale.

STATISTICAL DOWNSCALING MODEL WITH PRINCIPAL COMPONENT REGRESSION AND LATENT ROOT REGRESSION TO FORECAST RAINFALL IN PANGKEP REGENCY

Climate information, especially rainfall, is needed by various sectors in Indonesia, including the marine and fisheries sectors. Estimating high-resolution climate models continues to develop by involving global-scale climate variables, one of which is the global circulation model (GCM) output precipitation. Statistical downscaling (SD) relates global scale climate variables to local scales. Principal component regression (PCR) and latent root regression (LRR) techniques are statistical methods used in the SD model to overcome the high correlation between GCM data grids. PCR focuses on the variability in the predictor variables, while the LRR focuses on the variability between the response variables and predictors. This method was applied to Pangkep Regency rainfall data as a local scale response variable and GCM precipitation as a predictor variable (January 1999 to December 2020). This study aimed to obtain the number of principal component (PC) in the SD model and the forecast value of the 2020 rainfall data. In addition, the dummy variable resulting from K-means was used as a predictor variable in PCR and LRR. The result is that using the first 11-15 PC has a cumulative diversity proportion of 98%. Furthermore, by using the data for the 1999-2019 period, adding a dummy variable to the PCR can increase the accuracy of the model (the coefficient of determination is 92.27%-92.43%). However, LRR with and without dummy variables produces relatively the same model accuracy. In general, the LRR model is better at explaining the diversity of the Pangkep District rainfall data than the PCR model. The prediction of rainfall for the 2020 period at LRR with 13 PC is an accurate prediction based on the highest correlation value (0.97) and the lowest root mean square error prediction (75.17).

The Relative Warming Rates of Heat Events and Median Days in the Pacific Northwest from Observations and a Regional Climate Model

Abstract This paper examines the trends in hot summer days for the Pacific Northwest in observations and a regional climate model ensemble. Hot days are identified by the temperature threshold for several percentile values computed over 10-yr intervals (85th, 90th, and 95th percentiles and absolute maximum) to differentiate heat events of different intensities and are compared to the median temperature (50th percentile). For the stations analyzed, the observed rate of warming during hot days is not statistically different from the warming rate of median days since the 1950s. However, for projections to 2100, hot days show a statistically significant increase in the warming rate of the hottest days compared to the warming rate for median days. Depending on location, the 95th-percentile daily maximum temperature shows a warming rate of up to 0.2°C decade−1 above the median warming rate. The divergence in the trends of median and extreme temperature shows substantial regional variation depending on local terrain and coastlines. The warming trend during hot days is related to the unique circulation patterns during heat events, which respond to different feedbacks and amplifying effects in the land–atmosphere system from those that prevail during typical days. The regional climate model simulations are taken from an ensemble of the Weather Research and Forecasting (WRF) Model simulations forced by 12 global climate model simulations from phase 5 of the Climate Model Intercomparison Project (CMIP5) using the RCP8.5 emissions scenario and 12-km grid spacing. Significance Statement The heatwave of June 2021 had substantial societal and ecological impacts, illustrating vulnerability to record-shattering events. This paper addresses whether climate change can cause heat events in the Pacific Northwest to warm at a more rapid rate than typical days. Based on observations, in the recent past, the hottest days warmed at about the same rate as typical days in the recent past. However, results from a high-resolution climate model show a higher rate of warming during the warmest days relative to median days under the most aggressive future emissions scenario. This effect could cause heatwaves to become more intense relative to typical days by 2100, making adaptation to extreme events more difficult.

Mesoscale convective systems in the third pole region: Characteristics, mechanisms and impact on precipitation

The climate system of the Third Pole region, including the (TP) and its surroundings, is highly sensitive to global warming. Mesoscale convective systems (MCSs) are understood to be a vital component of this climate system. Driven by the monsoon circulation, surface heating, and large-scale and local moisture supply, they frequently occur during summer and mostly over the central and eastern TP as well as in the downstream regions. Further, MCSs have been highlighted as important contributors to total precipitation as they are efficient rain producers affecting water availability (seasonal precipitation) and potential flood risk (extreme precipitation) in the densely populated downstream regions. The availability of multi-decadal satellite observations and high-resolution climate model datasets has made it possible to study the role of MCSs in the under-observed TP water balance. However, the usage of different methods for MCS identification and the different focuses on specific subregions currently hamper a systematic and consistent assessment of the role played by MCSs and their impact on precipitation over the TP headwaters and its downstream regions. Here, we review observational and model studies of MCSs in the TP region within a common framework to elucidate their main characteristics, underlying mechanisms, and impact on seasonal and extreme precipitation. We also identify major knowledge gaps and provide suggestions on how these can be addressed using recently published high-resolution model datasets. Three important identified knowledge gaps are 1) the feedback of MCSs to other components of the TP climate system, 2) the impact of the changing climate on future MCS characteristics, and 3) the basin-scale assessment of flood and drought risks associated with changes in MCS frequency and intensity. A particularly promising tool to address these knowledge gaps are convection-permitting climate simulations. Therefore, the systematic evaluation of existing historical convection-permitting climate simulations over the TP is an urgent requirement for reliable future climate change assessments.

Improving the representation of shallow cumulus convection with the simplified-higher-order-closure–mass-flux (SHOC+MF v1.0) approach

Abstract. Parameterized boundary layer turbulence and moist convection remain some of the largest sources of uncertainty in general circulation models. High-resolution climate modeling aims to reduce that uncertainty by explicitly attempting to resolve deep moist convective motions. An example of such a model is the Simple Cloud-Resolving E3SM Atmosphere Model (SCREAM) with a target global resolution of 3.25 km, allowing for a more accurate representation of complex mesoscale deep convective dynamics. Yet, small-scale planetary boundary layer turbulence and shallow convection still need to be parameterized, which in SCREAM is accomplished through the turbulent-kinetic-energy-based (TKE-based) simplified higher-order closure (SHOC) – a simplified version of the assumed-double-Gaussian-PDF (probability density function) higher-order-closure method. In this paper, we implement a stochastic-multiplume mass-flux (MF) parameterization of dry and shallow convection in SCREAM to go beyond the limitations of double-Gaussian-PDF closures and couple it to SHOC (SHOC+MF). The new parameterization implemented in a single-column model type version of SCREAM produces results for two shallow cumulus convection cases (marine and continental shallow convection) that agree well with the reference data from large-eddy simulations, thus improving the general representation of the thermodynamic quantities and their turbulent fluxes as well as cloud macrophysics in the model. Furthermore, SHOC+MF parameterization shows weak sensitivity to the vertical grid resolution and model time step.

Ocean Mesoscale and Frontal-Scale Ocean–Atmosphere Interactions and Influence on Large-Scale Climate: A Review

Abstract Two decades of high-resolution satellite observations and climate modeling studies have indicated strong ocean–atmosphere coupled feedback mediated by ocean mesoscale processes, including semipermanent and meandrous SST fronts, mesoscale eddies, and filaments. The air–sea exchanges in latent heat, sensible heat, momentum, and carbon dioxide associated with this so-called mesoscale air–sea interaction are robust near the major western boundary currents, Southern Ocean fronts, and equatorial and coastal upwelling zones, but they are also ubiquitous over the global oceans wherever ocean mesoscale processes are active. Current theories, informed by rapidly advancing observational and modeling capabilities, have established the importance of mesoscale and frontal-scale air–sea interaction processes for understanding large-scale ocean circulation, biogeochemistry, and weather and climate variability. However, numerous challenges remain to accurately diagnose, observe, and simulate mesoscale air–sea interaction to quantify its impacts on large-scale processes. This article provides a comprehensive review of key aspects pertinent to mesoscale air–sea interaction, synthesizes current understanding with remaining gaps and uncertainties, and provides recommendations on theoretical, observational, and modeling strategies for future air–sea interaction research. Significance Statement Recent high-resolution satellite observations and climate models have shown a significant impact of coupled ocean–atmosphere interactions mediated by small-scale (mesoscale) ocean processes, including ocean eddies and fronts, on Earth’s climate. Ocean mesoscale-induced spatial temperature and current variability modulate the air–sea exchanges in heat, momentum, and mass (e.g., gases such as water vapor and carbon dioxide), altering coupled boundary layer processes. Studies suggest that skillful simulations and predictions of ocean circulation, biogeochemistry, and weather events and climate variability depend on accurate representation of the eddy-mediated air–sea interaction. However, numerous challenges remain in accurately diagnosing, observing, and simulating mesoscale air–sea interaction to quantify its large-scale impacts. This article synthesizes the latest understanding of mesoscale air–sea interaction, identifies remaining gaps and uncertainties, and provides recommendations on strategies for future ocean–weather–climate research.

On the Decadal and Multidecadal Variability of the Agulhas Current

Abstract The Agulhas Current (AC) is a critical component of global ocean circulation. However, due to a lack of multidecadal observations, it is not clear how the AC has changed in response to anthropogenic forcing. A recent observational study suggests a broadening and slight weakening of the AC in the past few decades, while others suggest a strengthening of the AC during the historical period. In this paper, we find substantial internal variability of the AC on decadal to multidecadal time scales in high-resolution models. We show that the AC consistently exhibits two modes of decadal and multidecadal (i.e., low frequency) variability in a series of high-resolution climate models: a uniform mode that is largely associated with changes in the AC strength and a dipole mode that is mainly related to width changes of the AC. We demonstrate that the uniform mode is mainly forced externally by the decadal variations of the wind field and presents a decline under global warming, suggesting a weakening of the AC in response to anthropogenic forcing. The dipole mode, on the other hand, is mainly due to internal dynamics and does not show a trend during the historical period. Using a quasigeostrophic model that captures the dipole mode, we attribute the dipole mode to low-frequency potential vorticity changes in the western boundary, driven by a divergence of relative potential vorticity due to eddy activity. Thus, our results present further context for the interpretation of the AC responses in a changing climate based on a short observational record.

Signatures of aerosol-induced decline in evapotranspiration over the Indo-Gangetic Plain during the recent decades

Evapotranspiration (ET) is the primary process of water transfer in the hydrological cycle over land and is linked to water, energy and carbon cycles. While the global hydrological cycle is expected to intensify in a warming climate with enhanced ET and precipitation, the magnitude and spatial distribution of regional scale response of ET to climate change remains uncertain. Here we present an analysis of in-situ observations of ET from 23 stations in India during 1979-2008, which shows that the annual ET has declined by about 9% over the humid sub-regions of the Indo-Gangetic Plain (IGP). Additional analysis from high-resolution climate model simulations and observed climate datasets lend support to the role of aerosol-induced solar-dimming in intensifying ET reductions, in a background of decreasing monsoon precipitation and soil-moisture levels, over the IGP

How well do high-resolution Global Climate Models (GCMs) simulate tropical cyclones in the Bay of Bengal?

Using six HighResMIP multi-ensemble GCMs (both the atmosphere-only and coupled versions) at 25 km resolution, the Tropical Cyclone (TC) activity over the Bay of Bengal (BoB) is examined in the present (1950–2014) climate. We use the Genesis Potential Index (GPI) to study the large-scale environmental conditions associated with the TC frequency in the models. Although the models struggle to reproduce the observed frequency and intensity of TCs, most models can capture the bimodal characteristics of the seasonal cycle of cyclones over the BoB (with fewer TCs during the pre-monsoon [April–May] than the post-monsoon [October–November] season). We find that GPI can capture the seasonal variation of the TC frequency over the BoB in both the observations and models. After calibrating the maximum sustained windspeeds in the models with IBTrACS, we find that like the observations the proportion of strong cyclones is also higher in the pre-monsoon than the post-monsoon. However, the inter-seasonal contrast of the proportion of strong cyclones between the pre-monsoon and post-monsoon seasons is reduced in almost all the models compared to the observations. The windshear term in GPI contributes the most to the model biases in all models during the post-monsoon season. This bias is caused by weakening of upper-level (200 hPa) easterlies in analysed models. During the pre-monsoon season, the environmental term in GPI dominating the model biases varies from model to model. When comparing the atmosphere-only and coupled versions of the models, a reduction of 0.5 °C in the sea surface temperature (SST) and a lowering of TC frequency occur in almost all the coupled models compared to their atmosphere-only counterparts.

Increases in extreme precipitation over the Northeast United States using high-resolution climate model simulations

Extreme precipitation is among the most destructive natural disasters. Simulating changes in regional extreme precipitation remains challenging, partially limited by climate models’ horizontal resolution. Here, we use an ensemble of high-resolution global climate model simulations to study September–November extreme precipitation over the Northeastern United States, where extremes have increased rapidly since the mid-1990s. We show that a model with 25 km horizontal resolution simulates much more realistic extreme precipitation than comparable models with 50 or 100 km resolution, including frequency, amplitude, and temporal variability. The 25 km model simulated trends are quantitatively consistent with observed trends over recent decades. We use the same model for future projections. By the mid-21st century, the model projects unprecedented rainfall events over the region, driven by increasing anthropogenic radiative forcing and distinguishable from natural variability. Very extreme events (>150 mm/day) may be six times more likely by 2100 than in the early 21st century.

Impact of climate change on wind energy across North America under climate change scenario RCP8.5

Wind energy is one of the renewable energies considered by many nations as a strategy to mitigate the impact of climate change. There are also impacts of climate change on wind energy. Previous studies investigated the effect of climate change on wind energy in North America by using future projections with spatial resolutions of about 100 km (i.e., around 1° × 1° simulation grid) and monthly to yearly average wind speeds or a high spatial resolution (< 50 km) but a few simulations (< 5). A coarse resolution can create biases and is insufficient to appreciate extremes. A smaller number of simulations (e.g., < 5) are inadequate for understanding the modeling uncertainties. To better appreciate the impact of climate change on wind energies in a future changing climate, 15 simulations from high-resolution Reginal Climate Models (RCMs) considering the Representative Concentration Pathway (RCP) 8.5 emission scenario were used in the present study. These datasets have a spatial resolution of about 0.4° and a temporal resolution of one hour from 1950 to 2100. The impact of climate change on wind energy was studied on a seasonal and yearly basis. The impact of climate change on design-level wind speeds and structural reliability across North America was also investigated.

Clear-air turbulence trends over the North Atlantic in high-resolution climate models

Clear-air turbulence (CAT) has a large impact on the aviation sector. Our current understanding of how CAT may increase with climate change in future is largely based on simulations from CMIP3 and CMIP5 global climate models (GCMs). However, these models have now been superseded by high-resolution CMIP6 GCMs, which for the first time have grid lengths at which individual turbulence patches may start to be resolved. Here we use a multi-model approach to quantify projected moderate CAT changes over the North Atlantic using CMIP6 models. The influence of the model resolution on CAT projections is analysed. Twenty-one CAT diagnostics are used, in order to represent uncertainties in CAT production mechanisms. Each diagnostic responds differently in time, but the majority display an increase in moderate CAT between 1950 and 2050. Although winter is historically the most turbulent season, there is strong multi-model agreement that autumn and summer will have the greatest overall relative increase in CAT frequency. By 2050, summers are projected to become as turbulent as 1950 winters and autumns. The global-mean seasonal near-surface temperature is used as a comparative metric. For every 1 °C of global near-surface warming, autumn, winter, spring, and summer are projected to have an average of 14%, 9%, 9%, and 14% more moderate CAT, respectively. Our results confirm that the aviation sector should prepare for a more turbulent future.

Anthropogenically-forced weakening of the Indian summer monsoon and enhancement of the western North Pacific tropical cyclogenesis

This study addresses the role of human-induced climate change on the interactions of convective activities between the Indian summer monsoon (ISM) and western North Pacific (WNP) regions - an important scientific issue which has been hitherto overlooked. We have examined this problem using two numerical experiments of a high-resolution climate model, with and without anthropogenic forcing (i.e., HIST and HISTNAT) for the historical period 1951–2005, supplemented by innovative diagnostics like causal network analysis. Our findings suggest that an anthropogenically-forced weakening of the ISM circulation tends to significantly enhance the genesis potential index (GPI) of the WNP tropical cyclones by 13.5% and associated convective activities, by reorienting the large-scale flow over the tropical Indo-Pacific in a manner that is conducive for enhancement of the WNP tropical cyclogenesis. Additionally, it is found that the probability of extremely low sea-level pressure (SLP) (&amp;lt;995.5 hPa) around Taiwan and Chinese mainland is significantly higher by 10.3% in the anthropogenically-forced simulation as compared to the natural run. Using the model outputs from HIST and HISTNAT, we also performed a causal effect network (CEN) analysis to understand the causal connections among the three indices involved in the ISM-WNP interactions (a) Indian monsoon circulation index (IMI) which is the difference in the area-averaged 850 hPa zonal winds between the boxes (40oE-80oE, 5oN-15oN) and (70oE-90oE, 20oN-30oN) (b) WNP tropical cyclone activity expressed as the genesis potential index (GPI) averaged over the region (120oE-180oE, 5oN-30oN) and (c) Tropical Indo-Pacific wind index (IPWND) which is based on the 850 hPa zonal winds averaged over the domain (100oE-130oE, 5oN-20oN). Results from the CEN analysis indicate that an anthropogenically-forced weakening of the IMI can lead to possible strengthening of GPI and IPWND with time-lags of 5 and 7 days, respectively. In general, it is noted that the causal relationships among IMI, GPI and IPWND are associated with shorter time-lags (∼4–9 days) in HIST and longer time-lags (∼19–28 days) in HISTNAT.

Investigating Extreme Snowfall Changes in China Based on an Ensemble of High-Resolution Regional Climate Models

Anthropogenically induced global warming intensifies the water cycle around the world. As a critical sector of the water cycle, snow depth and its related extremes greatly impact agriculture, animal husbandry, and food security, yet lack investigation. In this study, five high-resolution climate models are selected to simulate and project snow depth and its extremes over China. The simulation capabilities of models in reproducing the basic climate variables in winter are gauged in terms of spatial and temporal patterns over nine subregions. It is found that the driving global climate model (GCM) can contribute to similar patterns, while the different regional climate model (RCM) schemes lead to large variations in the snowfall accumulating on the land surface. The warming magnitude is larger under a higher representative concentration pathway (RCP) scenario (2.5 °C greater under RCP8.5 than RCP4.5). The distribution of ensemble mean winter precipitation changes is more fragmented because of the relatively low skill in reproducing water-related content in the climate system. The projected precipitation change is larger under RCP8.5 than under RCP4.5 due to the amplification of the hydrological cycle by temperature warming. The projected changes in the ensemble mean snow depth mainly occur over the Tibetan Plateau with a decreasing trend. Only several grids over the Himalayas Mountains and the upper stream of the Yarlung Zangbo River are projected with a slight increase in snow depth. Both the intensity and frequency of extreme snow events are projected to increase in Northeast China and Inner Mongolia, which are important agricultural and animal husbandry production areas in China. The reason behind this projection can be explained by the fact that the hydrological cycle intensified by temperature warming leads to excessive snowfall stacking up during winter. The changes in extreme snowfall events in the future will have a significant impact on China’s agricultural and animal husbandry production and threaten food security.

Climate-mine life cycle interactions for northern Canadian regions

This study quantifies the impacts of climate change on the mine life cycle (development, operation and closure phases) of 30 mines located in the northern regions of Canada. To this end, climate projections based on a five-member transient climate change simulation ensemble, performed using a state-of-the art regional climate model, spanning the 1991–2050 period, corresponding to the Representative Concentration Pathway 8.5 emission scenario are used. A reanalysis-driven simulation for the 1991–2010 period compared against available observations confirm suitability of the model for application in climate change simulations. Assessment of projected changes to mine-relevant climate variables that are important from structural integrity and operation perspectives reveal potential vulnerabilities and opportunities. Active layer thickness increases in the 0.3–2 m range in permafrost regions, coupled with increases in flood probability, as reflected in snow-melt rate increases in the 0.14–6.77% range and increases in the 100-year return levels of daily maximum rainfall in the 5–50% range, suggest potential impacts on the structural integrity of mine infrastructure, such as slope instability and foundation settlement of tailings dams, and supporting infrastructure such as ice/all-season roads. Increases in soil moisture, projected in the 0–11% range, at a few mines, suggest potential impacts on material handling systems, such as increases in the traction factor of the muck-haul and tire rolling resistance, that can lead to low productivity. Projected increases to wind speeds in the 5–10% range for the northernmost regions suggest potential impacts on the tailings management facility in terms of increases in tailings resuspension. Overall, this study identified northernmost and northeastern mines to be more vulnerable, with air/soil temperature, precipitation and wind speed being the most influential climate variables. This systematic study, for the first time, has identified potential vulnerabilities of northern Canadian mines, which can inform future high-resolution climate modelling and detailed at-site climate-mine interaction studies that is required for climate-change adaptation related decision-making.

Climate Change Impacts on Extreme Rainfall in Eastern Africa in a Convection-Permitting Climate Model

Abstract Climate change is expected to increase the frequency and intensity of rainfall extremes. Understanding future changes in rainfall is necessary for adaptation planning. Eastern Africa is vulnerable to rainfall extremes because of low adaptive capacity and high future population growth. Convection-permitting climate models have been found to better represent moderate (yearly) rainfall extremes than parameterized convection models, but there is limited analysis of rare extremes that occur less frequently than once per year. These events often have the largest socioeconomic impacts. We use extreme value theory and regional frequency analysis to quantify rare rainfall extremes over East Africa in a convection-permitting climate model (CP4A). We compare the results with its parameterized counterpart (P25), the Coordinated Regional Climate Downscaling Experiment for the African region (CORDEX-Africa) ensemble, and observations to understand how the convection parameterization impacts the results. We find that CP4A better matches observations than the parameterized models. With climate change, we find the parameterized convection models have unrealistically high changes in the shape parameter of the extreme value distribution, which controls the tail behavior (i.e., the most extreme events), leading to large increases in return levels of events with a return period of &gt;20 years. This suggests that parameterized convection models may not be suitable for looking at relative changes in rare rainfall events with climate change and that convection-permitting models should be preferred for this type of work. With the more realistic CP4A, RCP8.5 end-of-century climate change leads to 1-in-100-yr events becoming 1-in-23-yr events, which will necessitate serious adaptation efforts to avoid devastating socioeconomic impacts. Significance Statement We use a new, high-resolution climate model to examine how rare extreme rainfall events in East Africa might change in the future with climate change and compare the results with those from standard-resolution climate models. We find that the standard-resolution models have unrealistically large increases in rainfall for events that occur less frequently than every 20 years. The high-resolution model is more realistic and is required to illustrate possible future changes in rare rainfall extremes. Extreme events will become more common with climate change, and in the more realistic model we show that a 1-in-100-yr event may become a 1-in-23-yr event by the end of the century if greenhouse gas emissions are not significantly reduced.

A pragmatic approach to the combined effect of climate change and water abstraction on Lake Ziway water balance, Ethiopia

AbstractClimate change and ongoing human activity have been threatening Ethiopia's Lake Ziway's water balance. However, few studies have been conducted to investigate the combined effects of climate change and water withdrawal on the lake's water balance using climate change and water withdrawal for irrigation. We used high-resolution multiple climate models and Representative Concentration Pathways (RCP) scenarios to assess the impact of climatic variables for two future periods: 2021–2050 and 2051–2080. Rainfall and temperature data biases were corrected using power transformation and variance scaling methods, respectively. The Hydrologiska Byråns Vattenbalansavdelning (HBV) hydrological model was employed to simulate surface inflow into Lake Ziway from the Meki and Katar sub-catchments. The FAO CROPWAT model was used to estimate the irrigation water demand of major crops grown in the study area. The results indicate that future temperatures and wet season runoff levels are expected to rise. Under the worst climate change scenario, climate change and water withdrawal from the lake for agriculture may cause the lake level to drop by 25 cm per year, resulting in a 10 km2 surface area and 101 Mm3 volume reductions. Therefore, implementing preventive measures, proper planning and careful monitoring of lake water use is advised.