Coupled Model Intercomparison Phase Research Articles

Quantifying causal teleconnections to drought and fire risks in Indonesian Borneo

AbstractFires occurring over the peatlands in Indonesian Borneo accompanied by droughts have posed devastating impacts on human health, livelihoods, economy and the natural environment, and their prevention requires comprehensive understanding of climate‐associated risk. Although it is widely known that the droughts are associated with El Niño events, the onset process of El Niño and thus the drought precursors and their possible changes under the future climate are not clearly understood. Here, we use a causal network approach to quantify the strength of teleconnections to droughts at a seasonal timescale shown in observations and climate models. We portray two drivers of June‐July‐August (JJA) droughts identified through literature review and causal analysis, namely Niño 3.4 sea surface temperature (SST) in JJA (El Niño Southern Oscillation [abbreviated as ENSO]) and SST anomaly over the eastern North Pacific to the east of the Hawaiian Islands (abbreviated as Pacific SST) in March‐April‐May (MAM) period. We argue that the droughts are strongly linked to ENSO variability, with drier years corresponding to El Niño conditions. The droughts can be predicted with a lead time of 3 months based on their associations with Pacific SST, with higher SST preceding drier conditions. We find that under the SSP585 scenario, the Coupled Model Intercomparison Phase 6 (CMIP6) multi‐model ensembles show significant increase in both the maximum number of consecutive dry days in the Indonesian Borneo region in JJA (p = 0.006) and its linear association with Pacific SST in MAM (p = 0.001) from year 2061 to 2100 compared with the historical baseline. Some models are showing unrealistic amounts of JJA rainfall and underestimate drought risks in Indonesian Borneo and their teleconnections, owing to the underestimation of ENSO amplitude and overestimation of local convections. Our study strengthens the possibility of early warning triggers of fires and stresses the need for taking enhanced climate risk into consideration when formulating long‐term policies to eliminate fires.

Climate Change and Extreme Events in Northeast Atlantic and Azores Islands Region

In small island regions, the influence of climate change assumes particular relevance. In the Azores archipelago, made up of nine islands, the geographical circumstances, oceanic condition, territorial dispersion, land use model and other physiographic constraints reinforce and enhance the vulnerability of the islands to changes in current weather patterns. Coupled Model Intercomparison Phase 6 (CMIP6) projections are used for the northeast Atlantic region to evaluate daily extreme climate events in large scale for the Azores region. Results shows changes in the annual maximum number of consecutive dry days, the annual number of wet days, and especially in the annual number of tropical nights. Despite limitations due to the lack of spatial detail, the large-scale framework suggests changes that may be enhanced by topography, particularly with respect to precipitation. The conclusions point to the need to establish standard rules in the processes of design, reviewing and/or amending territorial management instruments at the municipal scale in the Autonomous Region of the Azores, with the goal of adapting to a different climate from the recent past.

Evaluation of CMIP6 HighResMIP for Hydrologic Modeling of Annual Maximum Discharge in Iowa

AbstractThe High‐Resolution Model Intercomparison Project (HighResMIP) experiments from the Coupled Model Intercomparison Phase 6 represent a broad effort to improve the resolution, and performance of climate models. The HighResMIP suite provides high spatial resolution (i.e., 25‐ and 50‐km) forcings that have been shown to improve the representation of climate processes. However, little is known about their suitability for hydrologic applications. We use outputs from the HighResMIP suite to simulate annual maximum discharge with the Hillslope‐Link Model (HLM) at ∼1,000 river communities across Iowa. First, we assess whether the runoff from the climate models can be directly routed through the river network model in HLM to estimate annual maximum discharge. Runoff‐based simulations can capture the empirical distribution of flood peaks in five of the 10 models/members assessed. Next, we force the HLM with precipitation, temperature, and potential evapotranspiration from HighResMIP models to simulate flood peaks, finding all models/members produce empirical distributions similar to our reference. However, significant biases exist in the model/member forcings as correct flood response is being generated for the wrong reason. To improve their suitability for community‐level assessment, we use nine statistical approaches to bias‐correct and downscale HighResMIP precipitation to a 4‐km resolution. The bias‐correction and downscaling of climate model precipitation performs well for all models/members. Furthermore, we do not find significant changes in the magnitude flood peak projections for Iowa based on the HLM forced with HighResMIP outputs, or based on routed runoff, while there are indications that the variability in flood peaks is projected to increase across the state.

Optimizing sowing window, cultivar choice, and plant density to boost maize yield under RCP8.5 climate scenario of CMIP5.

The impacts of climate change and possible adaptations to food security are a global concern and need greater focus in arid and semi-arid regions. It includes scenario of Coupled Model Intercomparison Phase 5 (CMIP-RCP8.5). For this purpose, two DSSAT maize models (CSM-CERES and CSM-IXIM) were calibrated and tested with two different maize cultivars namely Single Cross 10 (SC10) and Three Way Cross 324 (TW24) using a dataset of three growing seasons in Nile Delta. SC10 is a long-growing cultivar that is resistant to abiotic stresses, whereas TW24 is short and sensitive to such harsh conditions. The calibrated models were then employed to predict maize yield in baseline (1981-2010) and under future time slices (2030s, 2050s, and 2080s) using three Global Climate Models (GCMs) under CMIP5-RCP8.5 scenario. In addition, the use of various adaptation options as shifting planting date, increasing sowing density, and genotypes was included in crop models. Simulation analysis showed that, averaged over three GCMs and two crop models, the yield of late maturity cultivar (SC10) decreased by 4.1, 17.2, and 55.9% for the three time slices of 2030s, 2050s, and 2080s, respectively, compared to baseline yield (1981-2010). Such reduction increased with early maturity cultivar (TW24), recording 12.4, 40.6, and 71.3% for near (2030s), mid (2050s), and late century (2080s) respectively relative to baseline yield. The most suitable adaptation options included choosing a stress-resistant genotype, changing the planting date to plus or minus 30days from baseline planting date, and raising the sowing density to 9m-2 plants. These insights could minimize the potential reduction of climate change-induced yields by 39% by late century.

Eastern Bering Sea shelf environmental and lower trophic level responses to climate forcing: Results of dynamical downscaling from CMIP6

In this study we present projected changes in the Eastern Bering Sea shelf (EBS) biophysical processes in response to climate forcing scenarios from the Coupled Model Intercomparison Phase 6 (CMIP6). These changes are obtained by dynamical downscaling using a Bering Sea regional model. Surface atmospheric and ocean boundary forcing from three Earth System Models (ESMs) in CMIP6, and a low and a high emission scenario of Shared Socioeconomic Pathway (SSP126 and SSP585) of each of the ESMs are considered. Ensemble mean results suggest that, contrary to an anticipated increase in ocean stratification under warming, diminishing ice cover in response to climate forcing and resultant reduced surface freshening weakens EBS stratification in the melt season. Modeled ensemble mean phytoplankton and zooplankton biomass on the EBS exhibits subsurface maxima during the growing season; the amplitude of these maxima decreases with warming, along with a reduction in primary productivity and oxygen concentration over much of the EBS water column. Phenology of both phytoplankton and zooplankton biomass on the EBS shifts earlier, leading to an increase (decrease) in biomass averaged between April–July (August–November), while annually averaged biomass decreases under warming. Projected changes of primary and secondary plankton biomass at the end of the 21st century are not well separated between the SSP126 and SSP585 scenario in light of the large across model spread under each scenario. The projected ensemble mean warming amplitude of the EBS summer bottom temperature is largely unchanged between results forced by the Coupled Model Intercomparison Phase 5 Representative Concentration Pathway 8.5 (CMIP5 RCP8.5) and CMIP6 SSP585 scenarios. Likewise, the reduction rate of annual mean phytoplankton and large zooplankton biomass are comparable between RCP8.5 and SSP585 projections, even though the absolute amplitudes of biomass are sensitive to modeling parameters such as the solar irradiance attenuation curve. Hence, within the Bering Sea dynamical downscaling framework, projected long-term warming trends in EBS bottom temperature and plankton biomass reduction rates are robust responses to climate forcing.

Performance of the Taiwan Earth System Model in Simulating Climate Variability Compared With Observations and CMIP6 Model Simulations

AbstractThis study evaluates the performance of the Taiwan Earth System Model version 1 (TaiESM1) in simulating the observed climate variability in the historical simulation of the Coupled Model Intercomparison Phase 6 (CMIP6). TaiESM1 is developed on the basis of the Community Earth System Model version 1.2.2, with the inclusion of several new physical schemes and improvements in the atmosphere model. The new additions include an improved triggering function in the cumulus convection scheme, a revised distribution‐based formula in the cloud fraction scheme, a new aerosol scheme, and a unique scheme for three‐dimensional surface absorption of shortwave radiation that accounts for the influence of complex terrains. In contrast to the majority of model evaluation processes, which focus mainly on the climatological mean, this evaluation focuses on climate variability parameters, including the diurnal rainfall cycle, precipitation extremes, synoptic eddy activity, intraseasonal fluctuation, monsoon evolution, and interannual and multidecadal atmospheric and oceanic teleconnection patterns. A series of intercomparisons between the simulations of TaiESM1 and CMIP6 models and observations indicate that TaiESM1, a participating model in CMIP6, can realistically simulate the observed climate variability at various time scales and are among the leading CMIP6 models in terms of many key climate features.

A novel method to detect drought and flood years in Indian rainfall associated with weak and strong monsoon

The proposition of a new algorithm facilitates the predictability of weak/strong monsoons that lead to drought/flood events, respectively, in the Indian summer monsoon rainfall (ISMR). The proposed method estimates skewed Gaussian kernel distribution in the extreme values extracted from the rainfall series, and confidence levels of drought and flood years are obtained using bootstrap. Using the selected Coupled Model Intercomparison Phase 5 (CMIP5) simulations under representative concentration pathways (RCP) 8.5 scenario, the proposed method detects that extreme droughts (at 99% confidence level) in India are likely to occur in 2024 and 2027 in the early 21st century. Similarly, models project that 2031, 2032, and 2033 will be the most prominent flood years. It is projected that the probability of drought occurrence is likely to increase by 16%. In contrast, it is expected to diminish flood events by 11% in the future under projected global warming. Notably, our analysis reveals that 23.4% of grids covering ~30% of the Indian region are likely to experience increased frequency and intensity of droughts during 2020–2029, mainly covering the Northeast, Central, and Southern India. Furthermore, during this period, the Northeast and some parts in the North would experience floods over 29.6% (which covers ~ 39%) of the total grids. The proposed algorithm may be used for drought and flood monitoring over any geographical terrain.

Uncertainty of ENSO-amplitude projections in CMIP5 and CMIP6 models

There is a long-standing debate on how the El Niño/Southern Oscillation (ENSO) amplitude may change during the twenty-first century in response to global warming. Here we identify the sources of uncertainty in the ENSO amplitude projections in models participating in the Coupled Model Intercomparison Phase 5 (CMIP5) and Phase 6 (CMIP6), and quantify scenario uncertainty, model uncertainty and uncertainty due to internal variability. The model projections exhibit a large spread, ranging from increasing standard deviation of up to 0.6 °C to diminishing standard deviation of up to − 0.4 °C by the end of the twenty-first century. The ensemble-mean ENSO amplitude change is close to zero. Internal variability is the main contributor to the uncertainty during the first three decades; model uncertainty dominates thereafter, while scenario uncertainty is relatively small throughout the twenty-first century. The total uncertainty increases from CMIP5 to CMIP6: while model uncertainty is reduced, scenario uncertainty is considerably increased. The models with “realistic” ENSO dynamics have been analyzed separately and categorized into models with too small, moderate and too large ENSO amplitude in comparison to instrumental observations. The smallest uncertainties are observed in the sub-ensemble exhibiting realistic ENSO dynamics and moderate ENSO amplitude. However, the global warming signal in ENSO-amplitude change is undetectable in all sub-ensembles. The zonal wind-SST feedback is identified as an important factor determining ENSO amplitude change: global warming signal in ENSO amplitude and zonal wind-SST feedback strength are highly correlated across the CMIP5 and CMIP6 models.

LSTM integrated with Boruta-random forest optimiser for soil moisture estimation under RCP4.5 and RCP8.5 global warming scenarios

Future soil moisture (SM) estimation is a practically useful task for eco-hydrologists, agriculturists, and stakeholders in environment health monitoring to generate comprehensive understanding of hydro-physical and soil dynamic system. This paper demonstrates the capability of a hybridised long short-term memory (LSTM) predictive framework to emulate SM under global warming scenarios. The proposed model is developed by integrating Boruta-random forest (BRF) feature selection and capturing significant antecedent memory of SM behaviour were applied to estimate the future SM using Coupled Model Intercomparison Phase-5 (CMIP5) repository. The BRF is adapted to extract pertinent features in hydro-meteorological variables intrinsically related to SM, and therefore, is used to construct a hybridised deep learning (i.e., BRF-LSTM) model. To establish the viability of deep learning model for SM estimation until 2100, five stations closely matched to the global climate model grid are selected in Australia's Murray Darling Basin. The performance skill of BRF-LSTM model is compared against standalone models (i.e., LSTM, SVR, and MARS). The results showed that the hybrid deep learning model (i.e., BRF-LSTM) with a feature selection capability could significantly outperform the standalone models for both warming simulations. The proposed hybrid model also demonstrated superiority in SM estimation with over 95% of all predictive errors lying below 0.02 mm, and low relative root means square error (≈ 1.06% for RCP4.5 and ≈ 1.888% for RCP8.5) to outperform all the benchmark models. This study demonstrates the capability of LSTM algorithm coupled with BRF feature selection to simulate future soil moisture under climate change, and so, can be successfully implemented in hydrology, agriculture, soil use management and environmental management.

LONG-TERM PREDICTION OF CASPIAN SEA LEVEL UNDER CMIP6 SCENARIOS USING ARTIFICIAL NEURAL NETWORKS

Artificial Neural Network (ANN) is employed to predict the long-term Caspian Sea level (CSL). 114-year observed CSL data (1900-2014) and the precipitation and temperature of historical and future scenarios of Coupled Model Intercomparison Phase 6 (CMIP6) are used to predict the future fluctuations of CSL (2015-2050). The values of the statistical indices in training, validating and testing periods (1900-2014) indicate the efficiency of the ANN in reconstruction of the CSL. Considering the outputs of different climate change projections (CMIP6) and excluding the human interventions, the study predicts the CSL fluctuation range of -28 m to -26m until 2050.Recorded Presentation from the vICCE (YouTube Link): https://youtu.be/Kfj-gr65TR8

Assessment of precipitation extremes in India during the 21st century under SSP1-1.9 mitigation scenarios of CMIP6 GCMs

This study used a 30-year observed (1985–2014) precipitation, and the latest Coupled Model Intercomparison Phase 6 (CMIP6) Shared Socioeconomic Pathways (SSPs) based precipitation scenarios to assess the impact of precipitation extremes in near future (2020–2049) and far future (2070–2099) periods over India. For the assessment of precipitation extremes, standard extreme precipitation indices (EPIs) such as mean precipitation, cumulative wet days (CWD), cumulative dry days (CDD), number of rainy days (NR), precipitation events between 10 and 20 mm (P1020), precipitation events between 20 and 40 mm (P2040), precipitation events > 40 mm (PG40), annual maximum 1-day precipitation (Rx1day), and annual maximum 5-day precipitation (Rx5day) have been utilized. The percentage of change (or magnitude of change) has been calculated between CMIP6 scenarios and observed precipitation for all EPIs in the near future (NF-2020-2049) and far future (FF-2070-2099) times. The CMIP6 global climate models (GCMs) contain a significant amount of uncertainty. Thus, the quantile mapping approach has been applied to correct the bias in CMIP6 scenarios with reference to observed precipitation. This study also compares the results of precipitation extremes obtained in this study with previously published CMIP5 GCMs based precipitation extreme scenarios. This study’s observations clarified that a significant seasonal diversity and individuality existed among all the selected CMIP6 GCMs based precipitation scenarios (2020–2099) across India. As per EPIs, such as CDD based plots showed an increase (25–75%) in CDD over significant portions of India, while CWD plots showed an increase of CWD over a few areas of northwest, central, and northeastern India regions. In the case of event-based indices, all four CMIP6 GCMs showed some distinctive behavior, but the overall extremely high precipitation events such as PG40 showed a significant increase (25–80%) in both NF and FF times across India, except for a few grids.

Climatological, virological and sociological drivers of current and projected dengue fever outbreak dynamics in Sri Lanka.

The largest ever Sri Lankan dengue outbreak of 2017 provides an opportunity for investigating the relative contributions of climatological, epidemiological and sociological drivers on the epidemic patterns of this clinically important vector-borne disease. To do so, we develop a climatologically driven disease transmission framework for dengue virus using spatially resolved temperature and precipitation data as well as the time-series susceptible-infected-recovered (SIR) model. From this framework, we first demonstrate that the distinct climatological patterns encountered across the island play an important role in establishing the typical yearly temporal dynamics of dengue, but alone are unable to account for the epidemic case numbers observed in Sri Lanka during 2017. Using a simplified two-strain SIR model, we demonstrate that the re-introduction of a dengue virus serotype that had been largely absent from the island in previous years may have played an important role in driving the epidemic, and provide a discussion of the possible roles for extreme weather events and human mobility patterns on the outbreak dynamics. Lastly, we provide estimates for the future burden of dengue across Sri Lanka using the Coupled Model Intercomparison Phase 5 climate projections. Critically, we demonstrate that climatological and serological factors can act synergistically to yield greater projected case numbers than would be expected from the presence of a single driver alone. Altogether, this work provides a holistic framework for teasing apart and analysing the various complex drivers of vector-borne disease outbreak dynamics.

Intercomparison of the Surface Energy Partitioning in CMIP5 Simulations

The warming climate significantly modifies the global water cycle. Global evapotranspiration has increased over the past decades, yet climate models agree on the drying trend of land surface. In this study, we conducted an intercomparison analysis of the surface energy partitioning across Coupled Model Intercomparison Phase 5 (CMIP5) simulations and evaluated its behaviour with surface temperature and soil moisture anomalies, against the theoretically derived thermodynamic formula. Different responses over land and sea surfaces to elevated greenhouse gas emissions were found. Under the Representative Concentration Pathway of +8.5 W m−2 (RCP8.5) warming scenario, the multi-model mean relative efficiency anomaly from CMIP5 simulations is 3.83 and −0.12 over global sea and land, respectively. The significant anomaly over sea was captured by the thermodynamic solution based on the principle of maximum entropy production, with a mean relative error of 14.6%. The declining trend over land was also reproduced, but an accurate prediction of its small anomaly will require the inclusions of complex physical processes in future work. Despite increased potential evapotranspiration under rising temperatures, both CMIP5 simulations and thermodynamic principles suggest that the soil moisture-temperature feedback cannot support long-term enhanced evapotranspiration at the global scale. The dissipation of radiative forcing eventually shifts towards sensible heat flux and accelerates the warming over land, especially over South America and Europe.

Underlying mechanisms leading to El Niño-to-La Niña transition are unchanged under global warming

El Nino’s transitions play critical roles in modulating severe weather and climate events. Therefore, understanding the dynamic factors leading to El Nino’s transitions and its future projection is a great challenge in predicting the diverse socioeconomic influences of El Nino over the globe. This study focuses on two dynamic factors controlling the El Nino-to-La Nina transition from the present climate and to future climate, using the observation, the historical and the RCP8.5 simulations of Coupled Model Intercomparison phase 5 climate models. The first is the inter-basin coupling between the Indian Ocean and the western North Pacific through the subtropical high variability. The second is the enhanced sensitivity between sea surface temperature and a deep tropical convection in the central tropical Pacific during the El Nino’s developing phase. We show that the dynamic factors leading to El Nino-to-La Nina transition in the present climate are unchanged in spite of the increase of greenhouse gas concentrations. We argue that the two dynamic factors are strongly constrained by the climatological precipitation distribution over the central tropical Pacific and western North Pacific as little changed from the present climate to future climate. This implies that two dynamical processes leading to El Nino-to-La Nina transitions in the present climate will also play a robust role in global warming.

Curve number modifications and parameterization sensitivity analysis for reducing model uncertainty in simulated and projected streamflows in a Himalayan catchment

Climate essentially controls the supply of ecosystems, species ranges, and process rates on Earth. Modeling hydrological processes for hilly catchments dominated by snow cover and glaciers is complex and relies on improved calibration and uncertainty analysis methods to standardize the watershed based ecosystem management practices. In this study, a modified curve number (CN) approach has been employed to simulate streamflow and water yield at sub-catchment scale over hundred years utilizing Soil and Water Assessment Tool (SWAT), which relies on the main physical factors of the ecosystem such as landuse/landcover and soil. The model calibration and validation strength was evaluated using coefficients of determination (R2) and Nash-Sutcliffe Equation (NSE) objective functions. The uncertainties (percentage) occurred in the modeled streamflow were estimated using two optimization algorithms such as the Sequential Uncertainty Parameter Fitting Approach (SUFI2) and the Parametric Solution (ParaSol). A parameterization based sensitivity analysis was carried-out to recognize the most influencing model calibration parameters. Statistical downscaling of daily temperature and precipitation datasets was performed utilizing Coupled Model Intercomparison Phase Five (CMIP5) Global Circulation Models (GCMs) with their Representative Concentration Pathway (RCP) experiments. The downscaled temperature and precipitation were utilized to assess the climate change impact on streamflows at sub-catchment scale. The historical and projected scenarios of streamflow (at the outlet) and water yield (at sub-catchment scale) showed substantial variabilities in their amount in both temporal (1991–2100) and spatial scales (sub-catchment 1 to sub-catchment 7). The magnitude of change analysis confirmed a substantial increase in the water yield across all the sub-catchments over Himalaya. The percent of change analysis ensured that the magnitude of change of water yield is highly vulnerable in the Himalayan catchments. The GCMs based projected scenarios demonstrated a consistent increase in the streamflow at both outlets (e.g. Lachung and Chungthang). The overall results show a consistent increase in precipitation and water yield amount over the Himalayan catchments. The variable streamflow in terms of amount and intensity may disrupt the ecosystem services in the Himalayan catchments.

Use of observed temperature statistics in rankingCMIP5model performance over the Western Himalayan Region of India

ABSTRACTAssessing warming over the Western Himalayan Region (WHR) of India is challenging due to its limited station data availability and poor data quality. The missing values in the station data were replaced using the Multiple Imputation Chained Equation technique. Finally, 16 stations having continuous records during 1969–2009 were considered as the ‘reference stations’ for assessing the warming/cooling trends in addition to evaluate the Coupled Model Intercomparison, phase 5 (CMIP5), Global Circulation Model (GCM). Station data indicates winter (DJF) warming is higher and rapid (1.41 °C) than the other seasons and less warming was observed in the post‐monsoon (0.31 °C) season. Overall mean annual warming overWHRis ∼0.84 °C during 1969–2009. The performance of 34CMIP5models was evaluated based on three different criteria namely (1) mean seasonal cycle, (2) temporal trends and (3) spatial correlation between simulated and observed signals for common available period of 1969–2003 over the study area. Models are provided a final rank on the basis of the cumulative rank obtained in each of three approaches.CMCC‐CM,GISS‐E2‐HandMIROC 5are three top‐ranked models whileMIROC‐ESM,MIROC‐ESM‐CHEMand bcc‐csm1‐1 are three bottom‐ranked models over theWHR. The study also extended to judge whether the selected top‐ranked models perform well through two alternative data sources namely European Reanalysis (ERA)‐interim and Climate Research Unit (CRU), which have not used in the process of model evaluation. The spatial patterns of top‐rankedGCMare similar to the spatial pattern obtained throughERA‐interim andCRUwhile zoomed in toWHRbut bottom‐ranked models fail to reproduce such spatial patterns indicating the top‐rankedGCMswould offer more reliability for projecting future climate overWHR.

Historical and Projected Surface Temperature over India during the 20th and 21st century

Surface Temperature (ST) over India has increased by ~0.055 K/decade during 1860–2005 and follows the global warming trend. Here, the natural and external forcings (e.g., natural and anthropogenic) responsible for ST variability are studied from Coupled Model Inter-comparison phase 5 (CMIP5) models during the 20th century and projections during the 21st century along with seasonal variability. Greenhouse Gases (GHG) and Land Use (LU) are the major factors that gave rise to warming during the 20th century. Anthropogenic Aerosols (AA) have slowed down the warming rate. The CMIP5 projection over India shows a sharp increase in ST under Representative Concentration Pathways (RCP) 8.5 where it reaches a maximum of 5 K by the end of the 21st century. Under RCP2.6 emission scenarios, ST increases up to the year 2050 and decreases afterwards. The seasonal variability of ST during the 21st century shows significant increase during summer. Analysis of rare heat and cold events for 2080–2099 relative to a base period of 1986–2006 under RCP8.5 scenarios reveals that both are likely to increase substantially. However, by controlling the regional AA and LU change in India, a reduction in further warming over India region might be achieved.

Evolution of mean, variance and extremes in 21st century temperatures

Warming of the climate system can result in very large corresponding changes in the occurrence of climate extremes. Temperature extremes may occur due to a shift in the whole distribution, where there is an increase in the entire temperature probability distribution, or to changes in the shape of the distribution, such as an increase in variability causing a widening of the distribution. Understanding the precise characteristics of changes in temperature distributions in response to background warming is an important aspect of fully understanding changes in heat extremes and their associated impacts on human and ecosystem health. This study investigates projected 21st century changes in the characteristics of mean, maximum and minimum temperature on daily- and annual- timescales for various regions (Australia, Asia, Europe and North America) using data from seven models participating in the Coupled Model Intercomparison Phase 5 (CMIP5). Using the RCP8.5 experiment we show that an increase in mean temperature throughout the 21st century is a consistent feature of all models for each region. Changes in the variance of simulated temperatures are equivocal, with the sign and magnitude of variance changes in the 21st century varying in different models and regions. A quantile regression analysis demonstrates differences in upper and lower quantile slopes, relative to the mean, including a consistent skew in daily temperatures towards hot extremes. These potentially complex characteristics of temperature changes should not be overlooked, as temperature extremes are potentially more sensitive to changes in the variance and higher order moments than in the mean. Furthermore, a wider range of extreme temperature behaviour may have important consequences for various stakeholders, due to impacts on public health, agriculture and ecological systems.

The impacts of precipitating cloud radiative effects on ocean surface evaporation, precipitation, and ocean salinity in coupled GCM simulations

AbstractThe coupled global climate model (GCM) fidelity in representing upper ocean salinity including near sea surface bulk salinity (SSS) is evaluated in this study, with a focus on the Pacific Ocean. The systematic biases in ocean surface evaporation (E) minus precipitation (P) and SSS are found to be fairly similar in the twentieth century simulations of the Coupled Model Intercomparison Phase 3 (CMIP3) and Phase 5 (CMIP5) relative to the observations. One of the potential causes of the CMIP model biases is the missing representation of the radiative effects of precipitating hydrometeors (i.e., snow) in most CMIP models. To examine the radiative effect of cloud snow on SSS, sensitivity experiments with and without such effect are conducted by the National Center for Atmospheric Research‐coupled Community Earth System Model (CESM). This study investigates the difference in SSS between sensitivity experiments and its relationship with atmospheric circulation, E − P and air‐sea heat fluxes. It is found that the exclusion of the cloud snow radiative effect in CESM produces weaker Pacific trade winds, resulting in enhanced precipitation, reduced evaporation, and a reduction of the upper ocean salinity in the tropical and subtropical Pacific. The latter results in an improved comparison with climatological upper ocean bulk salinity. The introduction of cloud snow also altered the budget terms that maintain the time‐mean salinity in the mixed layer.

An Observational and Model Characterization of Vertical Structure of Wind Fields over Eastern United States: A Case Study of Sterling, Virginia

The performance of twenty GCMs that participated in the Coupled Model Intercomparison Phase 5 (CMIP5) is evaluated at Sterling, Virginia, by comparing model outputs with radiosonde observational dataset and reanalysis dataset. We evaluated CMIP5 models in their ability to simulate wind climatology, seasonal cycle, interannual variability, and trends at the pressure levels from 850 hPa to 30 hPa. We also addressed the question of the number of years required to detect statistically significant wind trends using radiosonde wind measurements. Our results show that CMIP5 models and reanalysis successfully reproduced the observed climatological annual mean zonal wind and wind speed vertical distribution. They also capture the observed seasonal zonal, meridional, and wind speed vertical distribution with stronger (weaker) wind during the winter (summer) season. However, there is some disagreement in the magnitude of vertical profiles among CMIP5 models, reanalysis, and radiosonde observation. Overall, the number of years to obtain statistically significant trend decreases with increasing pressure level except for upper troposphere. Although the vertical profile of interannual variability of CMIP5 models and reanalysis agree with the radiosonde observation, the wind trend is not statistically significant. This indicates that detection of trends on local scale is challenging because of small signal-to-noise ratio problems.