Multiple Global Climate Models Research Articles

High-resolution dynamical downscaling for regional climate projection in Central Asia based on bias-corrected multiple GCMs

Central Asia (CA) is among the most vulnerable regions to climate change due to the fragile ecosystems, frequent natural hazards, strained water resources, and accelerated glacier melting, which underscores the need to achieve robust projection of regional climate change. In this study, we applied three bias-corrected global climate models (GCMs) to conduct 9 km-resolution regional climate simulations in CA for the reference (1986–2005) and future (2031–2050) periods. The regional climate model (RCM) and GCM simulated daily temperature and precipitation are evaluated and the results show that both the bias-correction technique and dynamical downscaling method obtain numerous added values in reproducing the historical climate in CA, respect to the original GCMs. The former contributes more to reducing the biases of the climatology and the latter contributes more to capturing the spatial pattern. The RCM simulations indicate significant warming over CA in the near-term future, with the regional mean increase of annual mean temperature in a range of 1.63–2.01 ℃, relative to the reference period. Pronounced warming is detected north of ~ 45° N in CA from autumn to spring, which can be explained by the snow-albedo feedback. Enhanced warming projected in many mountains in the world is not found in CA, which is consistent with the study based on the reanalysis datasets during the past. Heatwave day frequency, number and maximum duration are expected to become more severe by 2031–2050. Changes in precipitation and Standard Precipitation Index (SPI) shows a wetter condition in CA in the coming decades. However, a fairer assessment of the wet/dry change with Standard Precipitation Evapotranspiration Index (SPEI) which takes into account of both precipitation and potential evapotranspiration reveals a drier condition. The climate change projections presented here serve as a robust scientific basis for assessment of future risk from climate change in CA.

Ensemble Drought Exposure Projection for Multifactorial Interactive Effects of Climate Change and Population Dynamics: Application to the Pearl River Basin

AbstractWith a changing climate, drought has become a common natural disaster. In our study, population exposure to drought over the Pearl River Basin (PRB) under climate change is investigated. Drought frequency is evaluated through the Standardized Precipitation Evapotranspiration Index (SPEI). The data needed for SPEI calculation are obtained based on the ensemble of multiple global climate model (GCM) outputs. Population exposure to drought for the future is assessed by combining drought frequency under two emission scenarios (RCP4.5 and RCP8.5) with three downscaled population scenarios (SSP1, SSP2, and SSP3). Moreover, the main contribution (and their interactions) of GCM, RCP, and SSP to the sources of uncertainty on population exposure projections is explored through multilevel factorial analysis. Results indicate that the temperature and precipitation would continually increase for the future, and the increase in drought frequency is more substantial during the 2080s than in the 2050s. Meanwhile, population exposure to drought accounts for 21.60% of the total population in 1976–2005 over the PRB area. During the 2050s, it would decrease to 11.98–12.28% under RCP4.5 and 14.15–14.40% under RCP8.5, respectively. By the 2080s, population exposure would slightly reduce under RCP4.5 and increase to 28.86–29.44% under RCP8.5. GCM is the primary uncertainty source of drought exposure in the 2050s, with contribution rates of 72.41%, 57.47%, 51.10%, and 78.71% to the four responses. In comparison, by the 2080s, RCP is the primary contributing factor, with contribution rates of 53.91%, 44.92%, 64.20%, and 48.00%, respectively.

Changes in precipitation variability across time scales in multiple global climate model large ensembles

Anthropogenic changes in the variability of precipitation stand to impact both natural and human systems in profound ways. Precipitation variability encompasses not only extremes like droughts and floods, but also the spectrum of precipitation which populates the times between these extremes. Understanding the changes in precipitation variability alongside changes in mean and extreme precipitation is essential in unraveling the hydrological cycle’s response to warming. We use a suite of state-of-the-art climate models, with each model consisting of a single-model initial-condition large ensemble (SMILE), yielding at least 15 individual realizations of equally likely evolutions of future climate state for each climate model. The SMILE framework allows for increased precision in estimating the evolving distribution of precipitation, allowing for forced changes in precipitation variability to be compared across climate models. We show that the scaling rates of precipitation variability, the relation between the rise in global temperature and changes in precipitation variability, are markedly robust across timescales from interannual to decadal. Over mid- and high latitudes, it is very likely that precipitation is increasing across the entire spectrum from means to extremes, as is precipitation variability across all timescales, and seasonally these changes can be amplified. Model or structural uncertainty is a prevailing uncertainty especially over the Tropics and Subtropics. We uncover that model-based estimates of historical interannual precipitation variability are sensitive to the number of ensemble members used, with ‘small’ initial-condition ensembles (of less than 30 members) systematically underestimating precipitation variability, highlighting the utility of the SMILE framework for the representation of the full precipitation distribution.

Evaluating the tradeoff between hydropower benefit and ecological interest under climate change: How will the water-energy-ecosystem nexus evolve in the upper Mekong basin?

Hydropower plant operation alters natural streamflow regimes, which leads to tradeoff between hydropower benefits and the needs of downstream river ecosystems. This study proposes a novel approach for evaluating how hydropower-ecology tradeoff will evolve under future climate change based on Pareto optimal fronts, and demonstrated its efficacy using a case study of the cascade hydropower plants and downstream river ecosystems of the upper Mekong basin (UMB), a representative transboundary river basin. Future climate projections from multiple global climate models (GCMs) are used. Results show that, although GCMs project a future overall increase in streamflow, the hydropower-ecology conflict will likely be exacerbated by increased streamflow variability. Nearly one third of the GCMs indicate the alleviated conflict between hydropower and ecosystems, one third show little apparent change, and the final third show aggravated conflicts. According to the pessimistic GCMs, maintaining ecological impact at historical levels in the future could result in a hydropower deficit for which thermal power would need to compensate, generating additional 1.33 MMT CO2e in greenhouse gas emissions per year. These results reveal the potential challenges facing hydropower and ecological development in the UMB in the future, and emphasize the importance of developing adaptive mitigation techniques under climate change.

Overall uncertainty of climate change impacts on watershed hydrology in China

AbstractThe hydrological projections provided by the outputs of Global Climate Models (GCMs) combining hydrological models include multi‐source uncertainties, which may challenge the formulation of relevant adaption and mitigation policies. In this paper, the overall uncertainty and the relative contribution of each uncertainty component were investigated for hydrological projections over 408 watersheds in China by using 3 shared socioeconomic pathway emission scenarios (SSP1‐2.6, SSP2‐4.5, and SSP5‐8.5), 21 GCMs, 8 bias correction methods, 4 hydrological models, and 2 sets of optimized hydrological model parameters. The results show that the total uncertainty (T) is mainly contributed by uncertainty related to global climate models (G), with the mean percentage ranging from 60.4 to 64.1%, followed by the interaction uncertainties among all components, with the mean percentage ranging from 22.0 to 26.4%. The uncertainty contribution of hydrological models (H) (6.1–9.4%) ranks third, followed by emission scenarios (S) (2.9–5.9%) and bias correction methods (B) (0.2–1.1%). The uncertainty contribution of the optimized hydrological model parameters (P) (0.2–0.3%) is almost negligible. In terms of spatial variability, the relative contribution of uncertainty related to global climate models (G) is the highest in the near future for northern China (67.5–70.6%) and in the far future for southern China (66.1–66.7%). However, it was found to be lower for the Tibetan Plateau and northwestern China (45.3–57.9%) in the near and far future. The relative contribution of hydrological model uncertainty is higher for southwestern and northwestern China and the Tibetan Plateau (7.2–19.5%) and lower for northern, eastern, and southern China (2.5–6.6%). This study highlights the importance of including multiple GCMs and hydrological models in hydrological impact studies to consider their overall uncertainty. The development of global climate models and hydrological models is still the best way to reduce the uncertainty of climate change impact studies.

Climate and hydrologic ensembling lead to differing streamflow and sediment yield predictions

Climate change is leading to alterations of the hydrologic cycle and sediment movement within watersheds, but the details and impacts of these changes are indeterminate. To reduce this uncertainty, many researchers create ensembles by averaging the projected temperature and precipitation from multiple global climate model (GCM) ensemble members before running these as forcing inputs through hydrologic models. There is little research quantifying if these ensembled climate scenarios produce similar hydrologic model results to those based on individual ensemble members. We created multiple sets of ensembled climate inputs for a pair of hydrologic and sediment yield models of adjacent watersheds that drain to the Great Lakes. We then compared the hydrologic and sediment results of the models forced by these ensembled climate scenarios with hydrologic ensembles created by running the individual climate ensemble members through the same hydrologic models. We found that, in all cases, the streamflow and sediment yield results are significantly different at the 5% confidence level and the ensembled climate scenarios can lead to systematic negative biases. We also looked at three subset hydrologic ensembles: all 10 CMIP5 ensemble members from the CSIRO mk3.6 model; a Representative ensemble with high, moderate, and low precipitation predictions; and a Best Fit ensemble based on GCM performance relative to historic climate. We found that the subset ensembles covered a large portion of the range of outputs for the whole set, while producing mean annual streamflows within 5.5% of the full hydrologic ensemble results and sediment yield and sediment discharge results within 12.2%.

An integrated multi‐GCMs Bayesian‐neural‐network hydrological analysis method for quantifying climate change impact on runoff of the Amu Darya River basin

AbstractAs one of the most pressing issues in the world, climate change has already caused evident impacts on natural and human systems (e.g., hydrological cycle, eco‐environment and socio‐economy) in recent decades. In this study, an integrated multi‐GCMs Bayesian‐neural‐network hydrological analysis (MBHA) method is developed for quantifying climate change impacts on runoff. MBHA incorporates multiple global climate models (multi‐GCMs), hydrological model (HBV‐light), and Bayesian neural network (BNN) within a general framework. MBHA can provide the reliable prediction for runoff as well as reflect the impact of climate change on data scarcity catchments. MBHA is applied to the Amu Darya River basin in Central Asia. Climate data are derived from multiple GCMs (i.e., GFDL‐ESM2G, HadGEM2‐AO and NorESM1‐M) under RCP4.5 and RCP8.5. Several findings can be summarized: (1) during 2021–2100, both precipitation and temperature would increase, with more precipitation falling as rain instead of snow; (2) by 2100, glacier areas are predicted to reduce by 62.3% (RCP4.5) and 71.9% (RCP8.5); (3) under RCP8.5, monthly runoff would increase by 11.2% in 2021–2060 and reduce by 5.0% in 2061–2100; this is because the glaciers would rapidly disappear with the rising temperature after 2060. The findings suggest that the shrinked glacier and the reduced runoff threaten the water availability especially in summer seasons as well as affect the agricultural irrigation in the downstream of the Amu Darya River.

Vulnerabilities of irrigated and rainfed corn to climate change in a humid climate in the Lower Mississippi Delta

The use of fossil fuels for energy needs increases atmospheric greenhouse gas (GHG) concentrations to levels that can significantly exacerbate the climate on earth. Assessing the vulnerability of regional crop production systems to such an altered climate in the future is essential for implementing appropriate adaptation and mitigation strategies for sustainable agriculture. We investigated the possible impacts of climate change (CC) projected by multiple global climate models (GCMs) on rainfed and irrigated corn (Zea mays L., a C4 plant) in the Lower Mississippi Delta region (LMD), USA. The CSM-CROPGRO-Maize v4.6 module in the RZWQM2 model (hereafter referred to as the “corn model”) was previously calibrated and validated for modeling corn at Stoneville, Mississippi, a representative location in the LMD was used. The CC scenarios considered in this study were ensembles of climate projections of multiple GCMs (97 ensemble members) that participated in the Climate Model Inter-comparison and Improvement Program 5. These CC scenarios were bias-corrected and spatially downscaled (BCSD) at the location for the years 2050 and 2080. Four representative GHG concentration pathways (RCP) 2.6, 4.5, 6.0, and 8.5 drove these CC scenarios. Under both irrigated and rainfed conditions, corn yield responses to enhanced CO2 were weak; thus, yield declined significantly in response to the enhanced air temperatures under all the RCP scenarios in both 2050 and 2080. The yield declines across RCPs ranged between 10 and 62% under irrigated conditions, and between 9 and 60% under rainfed conditions, mainly due to increased frequency of extreme temperatures and reduced crop durations. Water use efficiency declined between 22 and 150% under irrigated, and 8 and 54% under rainfed management. As an adaptation measure, planting corn up to 9 weeks earlier in the season, in general, failed to boost yields from increased crop duration and reduction in upper extreme air temperatures, as incidences of lower extreme temperatures also increased alarmingly. Development of cultivars that are more heat tolerant and produce higher yields under extreme temperatures would be required to combat corn yield decline in the region from climate change.

Climate change impact on water resources in Klong Yai River Basin, Thailand

Abstract. There is a 95 % scientific consensus that human activities contributed to climate change and altered hydrological processes. This study focused on a regional scale climate change impact assessment on water resources in the Klong Yai River basin in Thailand using multiple Global Climate Models (GCMs) under 3 scenarios of greenhouse gas emission called the Representative Concentration Pathway (RCPs), i.e., low (RCP2.6), intermediate (RCP4.5), and high (RCP8.5) scenarios. According to the projections, surface air temperature will increase relative to the reference period (1991–2000). Maximum surface air temperature is projected to increase around 0.3–1.35 ∘C. Precipitation in the near future (2017–2026 and 2027–2036) shows an increasing trend. The projected precipitation and temperature were used in the SWAT model for the Klong Yai River basin in order to assess the amount of inflow into reservoirs under the climate change conditions. Despite the future projections still have an uncertainty, but it is useful as a guideline for managing of our water resources and planning.

Implications of Projected Hydroclimatic Change for Tularemia Outbreaks in High-Risk Areas across Sweden.

Hydroclimatic change may affect the range of some infectious diseases, including tularemia. Previous studies have investigated associations between tularemia incidence and climate variables, with some also establishing quantitative statistical disease models based on historical data, but studies considering future climate projections are scarce. This study has used and combined hydro-climatic projection outputs from multiple global climate models (GCMs) in phase six of the Coupled Model Intercomparison Project (CMIP6), and site-specific, parameterized statistical tularemia models, which all imply some type of power-law scaling with preceding-year tularemia cases, to assess possible future trends in disease outbreaks for six counties across Sweden, known to include tularemia high-risk areas. Three radiative forcing (emissions) scenarios are considered for climate change projection until year 2100, incuding low (2.6 Wm−2), medium (4.5 Wm−2), and high (8.5 Wm−2) forcing. The results show highly divergent changes in future disease outbreaks among Swedish counties, depending primarily on site-specific type of the best-fit disease power-law scaling characteristics of (mostly positive, in one case negative) sub- or super-linearity. Results also show that scenarios of steeper future climate warming do not necessarily lead to steeper increase of future disease outbreaks. Along a latitudinal gradient, the likely most realistic medium climate forcing scenario indicates future disease decreases (intermittent or overall) for the relatively southern Swedish counties Örebro and Gävleborg (Ockelbo), respectively, and disease increases of considerable or high degree for the intermediate (Dalarna, Gävleborg (Ljusdal)) and more northern (Jämtland, Norrbotten; along with the more southern Värmland exception) counties, respectively.

Regional climate change projections from NA-CORDEX and their relation to climate sensitivity

The climate sensitivity of global climate models (GCMs) strongly influences projected climate change due to increased atmospheric carbon dioxide. Reasonably, the climate sensitivity of a GCM may be expected to affect dynamically downscaled projections. However, there has been little examination of the effect of the climate sensitivity of GCMs on regional climate model (RCM) ensembles. Therefore, we present projections of temperature and precipitation from the ensemble of projections produced as a part of the North American branch of the international Coordinated Regional Downscaling Experiment (NA-CORDEX) in the context of their relationship to the climate sensitivity of their parent GCMs. NA-CORDEX simulations were produced at 50-km and 25-km resolutions with multiple RCMs which downscaled multiple GCMs that spanned nearly the full range of climate sensitivity available in the CMIP5 archive. We show that climate sensitivity is a very important source of spread in the NA-CORDEX ensemble, particularly for temperature. Temperature projections correlate with driving GCM climate sensitivity annually and seasonally across North America not only at a continental scale but also at a local-to-regional scale. Importantly, the spread in temperature projections would be reduced if only low, mid, or high climate sensitivity simulations were considered, or if only the ensemble mean were considered. Precipitation projections correlate with climate sensitivity, but only at a continental scale during the cold season, due to the increasing influence of other processes at finer scales. Additionally, it is shown that the RCMs do alter the projection space sampled by their driving GCMs.

Climate change impact analysis using bias-corrected multiple global climate models on rice and wheat yield

Abstract Rice and wheat, two staple food grain crops, play a key role in farmers' income and food security. The response of these crops towards climate change is heterogeneous and uncertain. Therefore, it becomes essential to analyse the impact of climate change on these crops. An investigation was performed to analyse the impact of climate change on rice and wheat yield and to quantify the uncertainties in the yield predictions in West Bengal, India. The climatic projections from eight global climate models were used to simulate the rice and wheat yields in all districts of West Bengal. A quantile mapping method was used to correct systematic biases of daily rainfall, solar radiation and temperature. The corrected data were then used for driving crop environment and resource synthesis models for yield simulations. Results reveal that rice yield is expected to reduce by 7–9% in the 2020s, 8–14% in the 2050s and 8–15% in the 2080s, whereas wheat yield is expected to go down by 18–20% in the 2020s, 20–28% in the 2050s and 18–33% in the 2080s. These reductions signify that rice and wheat yield is more likely to decline under the future climate change condition, which may affect the regional food sustainability.

Future Precipitation-Driven Meteorological Drought Changes in the CMIP5 Multimodel Ensembles under 1.5°C and 2°C Global Warming

AbstractAnthropogenic forcing is anticipated to increase the magnitude and frequency of precipitation-induced extremes such as the increase in drought risks. However, the model-projected future changes in global droughts remain largely uncertain, particularly in the context of the Paris Agreement targets. Here, by using the standardized precipitation index (SPI), we present a multiscale global assessment of the precipitation-driven meteorological drought characteristics at the 1.5° and 2°C warming levels based on 28 CMIP5 global climate models (GCMs) under three representative concentration pathways scenarios (RCP2.6, RCP4.5, and RCP8.5). The results show large uncertainties in the timing reaching 1.5° and 2°C warming and the changes in drought characteristics among GCMs, especially at longer time scales and under higher RCP scenarios. The multi-GCM ensemble mean projects a general increase in drought frequency (Df) and area (Da) over North America, Europe, and northern Asia at both 1.5° and 2°C of global warming. The additional 0.5°C warming from 1.5° to 2°C is expected to result in a trend toward wetter climatic conditions for most global regions (e.g., North America, Europe, northern Asia, and northern Africa) due to the continuing increase in precipitation under the more intensified 2°C warming. In contrast, the increase in Df is projected only in some parts of southwest Asia, South America, southern Africa, and Australia. Our results highlight the need to consider multiple GCMs in drought projection studies under the context of the Paris Agreement targets to account for large model-dependent uncertainties.

Comparison of Changing Population Exposure to Droughts in River Basins of the Tarim and the Indus

AbstractDroughts are major, large‐scale, weather‐driven natural disasters, on the rise in the changing climate. We project changing population exposure to drought in two vulnerable, adjacent, basins of large rivers, the Tarim River Basin (TRB) and the Indus River Basin (IRB), for the future horizon 2021–2065. Drought events are assessed based on the outputs of multiple Global Climate Models, by applying the Standardized Precipitation Evapotranspiration Index (SPEI) and the Intensity‐Area‐Duration method (IAD). Future population exposure to droughts is evaluated by combining the drought events under three RCP scenarios (RCP2.6, RCP4.5, and RCP8.5) with the projected population from Shared Socioeconomic Pathways (SSPs), acknowledging the recent two‐child policy in China. Results show that frequency of drought events in both river basins could increase in the future, while increase in the TRB is stronger than in the IRB. The areal coverage of drought events in both river basins is projected to be greater in 2021–2065 than in 1961–2005. Increase of frequency and areal coverage in both basins is especially strong for the class of extreme drought events. According to the ensemble mean of multi‐GCMs, population exposure to droughts was 25.0% and 20.9% of the total population in the TRB and the IRB, respectively, in 1961–2005, and it is projected to increase by over 60% for the TRB and to increase by over 30% for the IRB, in 2021–2065.

Variations in extreme wave events near a South Pacific Island under global warming: case study of Tropical Cyclone Tomas

The intensification of tropical cyclones (TCs) and wind-induced ocean waves is expected to be amplified under global warming conditions. In 2010, strong TC Tomas approached the Fiji Islands and caused severe damage. Here, an ensemble simulation technique is combined with a pseudo-global warming (PGW) method to investigate future variations in TCs and wind-induced ocean waves. Ensemble PGW simulations were implemented using the weather research and forecasting (WRF) model with five different future projections. Hindcast and PGW simulations showed similar tracks of Tomas. In four PGW simulations, the central pressures of the simulated TCs decreased. Enhanced near-surface wind was recognized in three PGW simulations around the Fiji main island (Viti Levu). In the other two future simulations, the surface wind speed was weaker than the one in the present climate because of the slight eastward shift in the track and delayed development of the TC. WaveWatchIII (WW3) was applied for offshore wave simulations forced by the wind field obtained by WRF simulation results. In three future simulations, a clear increase in the maximum significant wave height (Hs) was found on the southeastern coast of Viti Levu. One future simulation yielded almost the same offshore wave characteristics as those under the present climate. In another future simulation, the ensemble mean Hs was as high as that in the present climate, but extremely large Hs values were found in several ensemble members. Future simulations using multiple global climate model (GCM) projections showed possible variations in TCs and wind-induced ocean waves which is useful for the risk assessment of various hazards.

Analyzing the impact of climate change on energy-economy-carbon nexus system in China

Climate change mitigation by reducing carbon dioxide emission becomes one of the major challenges for energy systems. In this study, a multi-GCM ensemble simulation and optimization approach is developed for analyzing the impact of climate change on China’s energy-economy-carbon nexus system under multiple uncertainties through integrating techniques of multiple global climate models, support-vector-regression, Monte Carlo simulation, and interval chance-constrained programming within a general framework. The developed approach can tackle multiple uncertainties existed in global climate models, random carbon dioxide emission and complex optimization process. Results disclose that (i) the national electricity demand would grow around 58.6% in the next 30 years under climate change; (ii) for climate change mitigation and sustainable development, fossil fuel would be gradually replaced by renewable energy (i.e. the share of fossil fuel to the total energy supply decreasing 22.5% and the share of electricity generated from renewable energy increasing 27.0% by 2050); (iii) compared to the peak value in 2030, carbon dioxide emission would reduce 15.1% by 2050, most reduction from coal-fired power generation; (iv) there is a tradeoff between carbon dioxide emission and system cost as p-level decreases from 0.15 to 0.01 (i.e. carbon dioxide emission can reduce 1.9% with 3.7% of raised system cost). It is desired for China to adjust its current energy supply structure for reducing carbon emission and seeking a low-carbon developmental path. The most effective way is to eliminate some small coal-fired units. A number of wind-power and solar-power projects are expected to be implemented due to their abundant potential markets.

Assessment of Future Climate Change Projections Using Multiple Global Climate Models

Nowadays, the hydrological cycle which alters river discharge and water availability is affected by climate change. Therefore, the understanding of climate change is curial for the security of hydrologic conditions of river basins. The main purpose of this study is to assess the projections of future climate across the Upper Ayeyarwady river basin for its sustainable development and management of water sector for this area. Global Ten climate Models available from CMIP5 represented by the IPCC for its fifth Assessment Report were bias corrected using linear scaling method to generate the model error. Among the GCMs, a suitable climate model for each station is selected based on the results of performance indicators (R2 and RMSE). Future climate data are projected based on the selected suitable climate models by using future climate scenarios: RCP2.6, RCP4.5, and RCP8.5. According to this study, future projection indicates to increase in precipitation amounts in the rainy and winter season and diminishes in summer season under all future scenarios. Based on the seasonal temperature changes analysis for all stations, the future temperature are predicted to steadily increase with higher rates during summer than the other two seasons and it can also be concluded that the monthly minimum temperature rise is a bit larger than the maximum temperature rise in all seasons.

Intercomparison of daily precipitation persistence in multiple global observations and climate models

Daily precipitation persistence is affected by various atmospheric and land processes and provides complementary information to precipitation amount statistics for understanding the precipitation dynamics. In this study, daily precipitation persistence is assessed in an exhaustive ensemble of observation-based daily precipitation datasets and evaluated in global climate model (GCM) simulations for the period of 2001–2013. Daily precipitation time series are first transformed into categorical time series of dry and wet spells with a 1 mm d−1 precipitation threshold. Subsequently, Pdd (Pww), defined as the probability of a dry (wet) day to be followed by another dry (wet) day is calculated to represent daily precipitation persistence. The analysis focuses on the long-term mean and interannual variability (IAV) of the two indices. Both multi-observation and multi-model means show higher values of Pdd than Pww. GCMs overestimate Pww with a relatively homogeneous spatial bias pattern. They overestimate Pdd in the Amazon and Central Africa but underestimate Pdd in several regions such as southern Argentina, western North America and the Tibetan Plateau. The IAV of both Pdd and Pww is generally underestimated in climate models, but more strongly for Pww. Overall, our results highlight systematic model errors in daily precipitation persistence that are substantially larger than the already considerable spread across observational products. These findings also provide insights on how precipitation persistence biases on a daily time scale relate to well-documented persistence biases at longer time scales in state-of-the-art GCMs.

Using climate projections to assess ecosystem vulnerability at scales relevant to managers

AbstractLand managers require information about the ongoing and potential effects of future climate to coordinate responses for ecosystems, species, and human communities at scales that are operationally meaningful. Our study focused on the vulnerability for all upland ecosystem types of Arizona and New Mexico in the southwestern United States. Local vulnerability across the two‐state area was represented by the level of departure for late 21st‐century climate from the characteristic pre‐1990 climate envelope of the ecosystem type at each given location, resulting in a probability surface of climate impacts for the two‐state area and an uncertainty assessment based on agreement in results among multiple global climate models. Though the results varied from one ecosystem type to the next, the majority of lands were forecast as high vulnerability and low uncertainty, reflecting significant agreement among climate model projections for the southwestern United States. We then tested our results in relation to ongoing ecological processes that have both regional and global change implications and discovered significant relationships with wildfire severity, upward tree species recruitment, and the encroachment of scrub into semidesert grassland. The testing helped determine the efficacy of the vulnerability surface, as a product of relatively high spatial and thematic resolution, in supporting local planning and management decisions. Most important, this study links climate and changes in vegetation by ecosystem processes that are already ongoing. The results affirm the value of climate model downscaling and show that this portable approach to correlative modeling has value in determining the location and magnitude of potential climate‐related impacts.

A multi-temporal analysis of streamflow using multiple CMIP5 GCMs in the Upper Ayerawaddy Basin, Myanmar

In this study, bias-corrected daily rainfall data of eight global climate models (GCMs) was used as input for a hydrologic model (Hydrological Engineering Center - Hydrological Modeling System (HEC-HMS)) to simulate daily streamflow in the Upper Ayerawaddy River basin (UARB), Myanmar. Monthly, seasonal, annual, and decadal mean flows, calculated for the baseline (1975–2014), were compared with projections for future periods (2040s: 2021–2060 and 2080s: 2061–2100) under two Representative Concentration Pathways (RCP 4.5 and RCP 8.5). The spread of low flows (10th and 25th percentile of daily flows) and high flows (75th, 90th, and 100th percentiles) were analyzed for each period. The ensemble of GCMs indicates an increase in mean monthly (except in October and November), seasonal (except post-monsoon), annual, and decadal rainfalls and corresponding flows in the UARB. Future low flows are expected to have high variability while high flows are expected to have higher means than that of baseline. The density distribution analysis of baseline and future flows reveals that future periods are likely to experience an increase in the magnitude of mean flows but a decrease in variability. Rainfall extremes indicated by 1-day maximum rainfall, 5-day consecutive maximum rainfall, and the number of extreme rainfall days reveals frequent wetter extremes in the UARB under future climate conditions. Extreme floods, as estimated by the frequency analysis of daily flows, are also expected to become more frequent during the future periods. These changes in flows can be attributed solely to climate change since the analyses did not account impacts of possible land use change and water resources development in the UARB. This study is a good starting point to assess future flows, and further research is recommended to address the limitations of this study for improved understanding and assessments that will prove useful for planning purposes in the study area.