Projected Air Temperature Research Articles

Projected changes in Brook Trout and Brown Trout distribution in Wisconsin streams in the mid-twenty-first century in response to climate change

Climate warming is a threat to the survival of fishes adapted to cold water. Brook Trout Salvelinus fontinalis and Brown Trout Salmo trutta are two cold-water species occurring in streams in Wisconsin, where climate change may make these species particularly vulnerable. Vulnerable trout populations need to be identified to aid in the development of adaptation strategies. We used web-based stream temperature and fish-distribution models in FishVis to predict current (late twentieth century) and project future (mid-twenty-first century) distributions of Brook Trout and Brown Trout. The models predict the suitability of habitat for trout in individual reaches using environmental variables in a geographic information system, including adjacent and upstream channel characteristics, surficial geology, landcover, and climate. Future projections of air temperature and precipitation were obtained from 13 general circulation models downscaled for Wisconsin. Currently, 34,251 km of streams are suitable for Brook Trout and 20,011 km for Brown Trout. The models project a decline of 68% (10,995 km) in stream habitat for Brook Trout and a decline of 32% (13,668 km) for Brown Trout. These projected declines, while substantial, were lower than earlier estimates because our models account for projected increased precipitation that may enhance groundwater inputs and partially offset higher air temperatures.

Quantifying the range of future glacier mass change projections caused by differences among observed past-climate datasets

Observed past climate data used as input in glacier models are expected to differ among datasets, particularly those for precipitation at high elevations. Differences among observed past climate datasets have not yet been described as a cause of uncertainty in projections of future changes in glacier mass, although uncertainty caused by varying future climate projections among general circulation models (GCMs) has often been discussed. Differences among observed past climate datasets are expected to propagate as uncertainty in future changes in glacier mass due to bias correction of GCMs and calibration of glacier models. We project ensemble future changes in the mass of glaciers in Asia through the year 2100 using a glacier model. A set of 18 combinations of inputs, including two observed past air temperature datasets, three observed past precipitation datasets, and future air temperature and precipitation projections from three GCMs were used. The uncertainty in projected changes in glacier mass was partitioned into three distinct sources: GCM uncertainty, observed past air temperature uncertainty, and observed past-precipitation uncertainty. Our findings indicate that, in addition to the differences in climate projections among GCMs, differences among observed past climate datasets propagate fractional uncertainties of about 15% into projected changes in glacier mass. The fractional uncertainty associated with observed past precipitation was 33–50% that of the observed air temperature. Differences in observed past air temperatures and precipitation did not propagate equally into the ultimate uncertainty of glacier mass projection when ablation was dominant.

Responses of a native and a recent invader snail to warming and dry conditions: the case of the lower Ebro River

Aquatic habitats have been highly modified by human actions that reduce their native diversity and create conditions suitable for tolerant alien species. Pomacea maculata was detected in 2009 in both the alluvial plain and the final stretch of the Ebro River. Since then, a permanent population has stabilized in the littoral area of the river where the water level fluctuates according to the river discharge. Melanopsis tricarinata is an endemic snail species highly affected by the reduction in its natural habitat. Currently, the two species do not share the same reaches in the river, but the possibility exists, as the distribution of the P. maculata is constantly increasing. This study aims to analyse the diets and to assess the responses of both snails to global change. The diet of both species was analysed in the field and their responses to water warming and dryness compared under laboratory conditions. This study includes the calculation of future river water temperatures based on air temperature projections. In addition, based on water discharge management scenarios, the study estimated the increase in dry river bed area. The diet of both snail species was similar and based on Cladophora. P. maculata better resisted high temperatures and dry conditions than M. tricarinata. The projections of water temperatures showed an increase in daily temperatures, especially in summer. The hydraulic model suggested that a relevant increase in dry river bed areas will occur. Overall, these results provide insight into the global change factors that could favour P. maculata spread in the river and the reduction in suitable habitat for M. tricarinata and will be useful for future decisions of water discharge management.

Projected effects of temperature changes on the Italian Western Tauri glaciers (Eastern Alps)

ABSTRACTA linear model was used to investigate the behaviour of the Italian Western Tauri glaciers as a result of temperature changes projected for the 21st century. The model estimates the temperature variations once the glacier snout length variations are known and vice versa: it estimates the glacier snout length variations once the air temperature variations are known or predicted. The 46 glaciers of the Italian Western Tauri, 35 (76%) of which have areas smaller than 0.5 km2 and only 7 (15%) larger than 1 km2, are mostly mountain type glaciers and only three are presently valley glaciers. The model has been forced by the air temperature projections of the A1B emission scenario, which indicates an increase in temperature of 2.7°C from 2015 to 2100. The results show a shortening of more than 35% for mountain glaciers by 2100 with a surface loss of more than 60% and smaller reductions for valley glaciers. The consequent fragmentation into smaller units would lead to the extinction of 95% of the existing glaciers by the end of the century, possibly leaving only the valley glaciers surviving.

Using a Hydrologic Model to Assess the Performance of Regional Climate Models in a Semi-Arid Watershed in Brazil

This study assessed the impact of climate change on the hydrological regime of the Paraguaçu river basin, northeastern Brazil. Hydrological impact simulations were conducted using the Soil and Water Assessment Tool (SWAT) for 2020–2040. Precipitation and surface air temperature projections from two Regional Climate Models (Eta-HadGEM2-ES and Eta-MIROC5) based on IPCC5—RCP 4.5 and 8.5 scenarios were used as inputs after first applying two bias correction methods (linear scaling—LS and distribution mapping—DM). The analysis of the impact of climate change on streamflow was done by comparing the maximum, average and reference (Q90) flows of the simulated and observed streamflow records. This study found that both methods were able to correct the climate projection bias, but the DM method showed larger distortion when applied to future scenarios. Climate projections from the Eta-HadGEM2-ES (LS) model showed significant reductions of mean monthly streamflow for all time periods under both RCP 4.5 and 8.5. The Eta-MIROC5 (LS) model showed a lower reduction of the simulated mean monthly streamflow under RCP 4.5 and a decrease of streamflow under RCP 8.5, similar to the Eta-HadGEM2-ES model results. The results of this study provide information for guiding future water resource management in the Paraguaçu River Basin and show that the bias correction algorithm also plays a significant role when assessing climate model estimates and their applicability to hydrological modelling.

ЗМІНА КЛІМАТУ ТА УКРАЇНСЬКІ МІСТА: ПРОЯВИ ТА ПРОЕКЦІЇ ДО КІНЦЯ ХХІ СТОЛІТТЯ НА ОСНОВІ RCP-СЦЕНАРІЇВ

The features of the manifestation of climate change in the cities of Ukraine are investigated. It has been established that over the past decades mean annual air temperature in big cities has increased on 0.7–1.2°С, compare to the normal climatic period (1961–1990). The most significant increase in the mean monthly air temperature in the modern period in Ukrainian cities was recorded in January and in the summer months. An increase in the recurrence of tropical nights (when the air temperature did not drop below 20.0°C) and heat waves (HW) was revealed. In all studied cities located in different regions of Ukraine (with the exception of Kharkiv and Kyiv), the number of HW cases in the modern period (1991–2015) has increased two or more times compared with 1961–1990. The most vulnerable in the modern period to the effects of HW are the cities of the southern regions of Ukraine. No HW cases were recorded in Odessa during the period 1961–1990, and 14 cases have been identified in the modern period; the frequency of HW in Zaporizhzhya and Kherson increased from 3 to 22 cases. A change was found in the distribution of precipitation over the seasons and the nature of their precipitation: with a decrease in the number of cases of precipitation, their intensity increases. Based on RCP-scenarios, projections of air temperature for the studied cities were made until the end of the 21st century. According to the calculations for all scenarios, the average annual air temperature will increase gradually in all regions and cities of Ukraine and will increase on average from 2.0–2.5°C in the low-end scenario (RCP 4.5) to 5.0–6.0°C in the high-end scenario (RCP 8.5). Thus, the results of the study indicate the need to analyze the vulnerability of Ukrainian cities to climate change and can be used both to carry out such assessment and to develop measures and plans for adaptation to climate change.

Rainfall and air temperature projections for Sharjah City, United Arab Emirates

Rainfall and air temperature projections for Sharjah City, United Arab Emirates

Rainfall and air temperature projections for Sharjah City, United Arab Emirates

Climate change has significant effects on hydrological cycle and water resources. Sharjah City in the United Arab Emirates (UAE) is located in one of the most water-scarce regions of the world. Despite the importance of climate change projection and impact studies, those studies specifically for Sharjah City are rare. In this study, first, monthly rainfall and temperature projections were generated for near and far future. Then, trend analysis was conducted to detect trends in future rainfall and temperature projections for Sharjah City. Findings of this study showed that mean air temperature in Sharjah City is expected to rise up to 12-17% for near and far future respectively, whereas average annual rainfall is projected to decrease in the range of 1-53%. Trend analysis showed significant increasing air temperature trends and non-significant decreasing rainfall trends. This study will contribute to climate change impact and policy making studies in Sharjah City.

An evaluation method for uncertainties in regional climate projections

Regional climate projections inevitably inherit uncertainties from general circulation models (GCMs). We therefore propose a new approach for identifying the dominant uncertainties. This approach employs the downscaling procedure by Adachi et al. to the uncertainty problem using multiple GCM projections. The mean state of the large‐scale atmospheric states and the deviation from this mean state are the two uncertainty factors considered here, which are provided by a GCM. These two factors are referred to as climatology and perturbation components, respectively. To demonstrate the effectiveness in identifying these uncertainty factors using the proposed approach, a regional projection of summertime climate in western Japan is conducted using four different future climate data that are calculated using an atmospheric GCM with different sea surface temperatures. Results show that the variability in surface air temperature projections is reasonably derived from the climatology uncertainty, whereas the variability in precipitation projections is equally influenced by the climatology and perturbation uncertainties. Both the climatology and perturbation uncertainties should therefore be considered when analysing regional climate projections.

Evaluating seasonal and regional calibration of temperature-based methods for estimating potential evaporation in Ontario

Development of appropriate water management strategies in the context of projected air temperature increases under a changing climate necessitates assessing how increases in atmospheric demand for water might constrain future water availability. This is most easily assessed using estimates of potential evaporation (PE). Most temperature-based methods for estimating PE have been calibrated in different landscapes in more southern locations using a singular calibration coefficient. This study assessed whether regionally calibrated temperature-based models improve estimates of PE in Ontario and whether further improvements could be made by adjusting coefficients monthly to address a marked seasonality in Ontario or spatially to address the province’s size and varied landscape. Monthly calibration coefficients were calculated for four temperature-based methods (Thornthwaite, Blaney–Criddle, Hamon and Hargreaves–Samani) using climate data and evaporation estimates from 18 pan evaporation stations in Ontario. Calibrated models were then evaluated against an additional 19 pan evaporation stations by comparing estimates of PE calculated using the adjusted and literature-derived coefficients with pan evaporation estimates. Finally, PE estimated using adjusted coefficients was compared to evaporation (E) estimated using a water balance approach for 64 watersheds across Ontario. As anticipated, the regional calibration process increased the accuracy of the PE estimates relative to those calculated using published model coefficients. Use of monthly model coefficients addressed the marked seasonality of evaporation in Ontario and resulted in improved PE estimates. Adjusted model coefficients varied spatially but the paucity of measurement stations precluded further regionalization. PE estimates improved using adjusted coefficients, varied less between models, and were highly correlated with water balance estimates of E. The Hargreaves–Samani model produced the best estimates of PE using unadjusted and adjusted coefficients and was most highly correlated with long-term water balance estimates of E.

Assessment of CMIP5 global climate models and projected changes in surface air temperature over the Arabian Peninsula in the twenty-first century

This study examines the surface air temperature projections and associated uncertainties over the Arabian Peninsula by using data from the Coupled Model Intercomparison Project Phase 5 (CMIP5) under the Representative Concentration Pathways RCP4.5 and RCP8.5 scenarios. The 30-member CMIP5 mean multi-model ensemble (MME) reveals a significant (at the 99% level) increase in temperature by the end of the twenty-first century of around 2.28 (5.53) °C/100 years under the RCP4.5 (RCP8.5) scenarios. The warming as well as the uncertainty associated with the temperature projections over the Arabian Peninsula will increase with time up to the end of the twenty-first century. In the near future (2021–2050), both RCPs (RCP4.5 and RCP8.5) reveal similar increases (~ 2 to 2.5 °C) in temperature compared to the present climate (1976–2005). For the period 2070–2099, the temperatures are projected to be from 2.55 ± 1.58 (4.89 ± 1.58) °C relative to the present climate under RCP4.5 (RCP8.5), while the associated uncertainty ranges from − 1.38 to 5.95 (0.57 to 7.9) °C. Under RCP4.5 (RCP8.5), all models reach 2 °C in the Arabian Peninsula in the twenty-first century relative to 1976–2005 from 2023 (2030) to 2062 (2049). The warming over the Arabian Peninsula is projected to be asymmetric that will vary both spatially and seasonally. A strong increase in the annual mean temperature over the central and northwestern parts of the Arabian Peninsula comes mainly from the summer and autumn season. The projected warming rate is higher in the autumn compared to the other seasons during the twenty-first century. The decadal and centurial warming rates are lower in winter and higher in autumn.

Projections of the future disappearance of the Quelccaya Ice Cap in the Central Andes

We analyze the future state of Quelccaya Ice Cap (QIC), the world’s largest tropical ice cap with a summit elevation of 5680 m a.s.l., which, in terms of its elevation range (~5300–5680 m a.s.l.), is representative of many low-elevation glacierized sites in the tropical Andes. CMIP5 model projections of air temperature (Ta) at QIC indicate a warming of about 2.4 °C and 5.4 °C (respectively) for RCP4.5 and RCP8.5 scenarios by the end of the 21st century, resulting in a pronounced increase in freezing level height (FLH). The impact of this warming on the QIC was quantified using equilibrium-line altitude (ELA) projections. The change in the ELA was quantified based on an empirical ELA–FLH relationship, and calibrated with observations of the highest annual snowline altitude (SLA) derived from LANDSAT data. Results show that from the mid-2050s onwards, the ELA will be located above the QIC summit in the RCP8.5 scenario. At that time, surface mass balance at QIC and most tropical glaciers at similar elevations will become increasingly negative, leading to their eventual complete disappearance. Our analysis further corroborates that elevation-dependent warming (EDW) contributes significantly to the enhanced warming over the QIC, and that EDW at Quelccaya depends on the rate of anthropogenic forcing.

Bias correction and projection of surface air temperature in LMDZ multiple simulation over central and eastern China

Based on LMDZ4 daily temperature dataset, equidistant cumulative distribution function matching method (EDCDFm) and cumulative distribution function-transform method (CDF-t) are used to evaluate the ability of models in simulating extreme temperature over central and eastern China. The future temperature change is then projected. The results show that the EDCDFm and CDF-t methods function effectively correct the spatial distribution of daily mean temperature and extreme temperature, significantly reduce the biases of the model simulation and effectively improve the capacity of models for spatial pattern of extreme temperature. However, the cold bias of the CDF-t method in winter is obviously higher than that of the EDCDFm method, and the temperature change curve of the EDCDFm method is closer to the observation than that of the CDF-t method. The projection based on the EDCDFm method shows that under the RCP4.5 emission scenario, the temperature in the study area shows a warming trend. Relative to 1986–2005, the mean temperature is projected to increase by 0.76, 1.84, and 2.10 °C during 2017–2036, 2046–2065, and 2080–2099, respectively. The spatial change for the mean, maximum, and minimum temperature in the three future periods have good consistency; warming in northern China is higher than that in the south. Uncertainties in temperature projection are large in the Tibetan Plateau and Sichuan Basin. Frost days decrease significantly, especially in the Tibetan Plateau, and the frost days in the three periods decrease by more than 15, 30, and 40 d, respectively. The variation of heat wave indice is the smallest; the increase of heat wave is mainly in eastern China, and the increase in South China is more than 2 d. Besides, under the global warming of 1.5 °C and 2 °C, the response characteristics of extreme temperature over central and eastern China are also analyzed. The results show that the mean temperature, maximum temperature and minimum temperature in the study area increase by more than 0.75 °C under 1.5 °C target and over 1.25 °C under 2 °C target, especially in the northwestern China and the Tibetan Plateau, relative to 1986–2005. Additionally, comparing two warming targets, the difference of three temperature indices in parts of northeastern China is over 1.5 °C, while more than 3 d for heat wave.

Thermal and hydrologic responses to climate change predict marked alterations in boreal stream invertebrate assemblages.

Air temperature at the northernmost latitudes is predicted to increase steeply and precipitation to become more variable by the end of the 21st century, resulting in altered thermal and hydrological regimes. We applied five climate scenarios to predict the future (2070-2100) benthic macroinvertebrate assemblages at 239 near-pristine sites across Finland (ca. 1200km latitudinal span). We used a multitaxon distribution model with air temperature and modeled daily flow as predictors. As expected, projected air temperature increased the most in northernmost Finland. Predicted taxonomic richness also increased the most in northern Finland, congruent with the predicted northwards shift of many species' distributions. Compositional changes were predicted to be high even without changes in richness, suggesting that species replacement may be the main mechanism causing climate-induced changes in macroinvertebrate assemblages. Northern streams were predicted to lose much of the seasonality of their flow regimes, causing potentially marked changes in stream benthic assemblages. Sites with the highest loss of seasonality were predicted to support future assemblages that deviate most in compositional similarity from the present-day assemblages. Macroinvertebrate assemblages were also predicted to change more in headwaters than in larger streams, as headwaters were particularly sensitive to changes in flow patterns. Our results emphasize the importance of focusing protection and mitigation on headwater streams with high-flow seasonality because of their vulnerability to climate change.

Future changes over the Himalayas: Mean temperature

Abstract An assessment of the projection of near surface air temperature over the Himalayan region from the COordinated Regional Climate Downscaling EXperiment- South Asia (hereafter, CORDEX-SA) regional climate model (RCM) experiments have been carried out for different Representative Concentration Pathway (RCP) scenarios. The purpose of this study is to assess the probable future changes in the mean temperature climatology and its long term trend for different seasons under greenhouse gas forcing scenarios for different seasons till the end of 21st century. A number of statistical measures such as changes in mean climatology, long term trend and probability distribution function have been used in order to detect the signals of changes in climate. Moreover, the associated uncertainties among different model experiments and their ensemble in space, time and different seasons in particular have been quantified. Despite of strong cold bias in the model experiments over Himalayan region (Nengker et al., 2017), statistically significant strong rate of warming (0.03–0.09 °C/year) across all the seasons and RCPs have been projected by all the models and their ensemble. Season specific response towards the warming is indicated by ensemble under future climate while ON season shows comparable magnitude of warming than DJF. Such warming intensifies with the increase in the radiative forcing under a range of greenhouse gas scenarios from RCP2.6 to RCP8.5. In addition to this, a wide range of spatial variability and disagreements in the trend magnitude between different models describes the uncertainty associated with the model projections and scenarios. A substantial seasonal response to warming with respect to elevation was also found, as DJF season followed by ON portrays highest rate of warming, specifically at higher elevation sites such as western Himalayas and northern part of central Himalayas. The different elevation classes respond differently to the projected future warming under different RCPs and seasons. Such higher warming during DJF may have consequences as changes in the fractional distribution of the solid and liquid precipitation as well as the melting of the glacial deposits with ultimately affecting the streamflow response and water resources in the downstream areas.

Future changes over the Himalayas: Maximum and minimum temperature

Abstract An assessment of the projection of minimum and maximum air temperature over the Indian Himalayan region (IHR) from the COordinated Regional Climate Downscaling EXperiment- South Asia (hereafter, CORDEX-SA) regional climate model (RCM) experiments have been carried out under two different Representative Concentration Pathway (RCP) scenarios. The major aim of this study is to assess the probable future changes in the minimum and maximum climatology and its long-term trend under different RCPs along with the elevation dependent warming over the IHR. A number of statistical analysis such as changes in mean climatology, long-term spatial trend and probability distribution function are carried out to detect the signals of changes in climate. The study also tries to quantify the uncertainties associated with different model experiments and their ensemble in space, time and for different seasons. The model experiments and their ensemble show prominent cold bias over Himalayas for present climate. However, statistically significant higher warming rate (0.23–0.52 °C/decade) for both minimum and maximum air temperature (Tmin and Tmax) is observed for all the seasons under both RCPs. The rate of warming intensifies with the increase in the radiative forcing under a range of greenhouse gas scenarios starting from RCP4.5 to RCP8.5. In addition to this, a wide range of spatial variability and disagreements in the magnitude of trend between different models describes the uncertainty associated with the model projections and scenarios. The projected rate of increase of Tmin may destabilize the snow formation at the higher altitudes in the northern and western parts of Himalayan region, while rising trend of Tmax over southern flank may effectively melt more snow cover. Such combined effect of rising trend of Tmin and Tmax may pose a potential threat to the glacial deposits. The overall trend of Diurnal temperature range (DTR) portrays increasing trend across entire area with highest magnitude under RCP8.5. This higher rate of increase is imparted from the predominant rise of Tmax as compared to Tmin.

Historical Trends, Drivers, and Future Projections of Ice Phenology in Small North Temperate Lakes in the Laurentian Great Lakes Region

Lake ice phenology (timing of ice breakup and freeze up) is a sensitive indicator of climate. We acquired time series of lake ice breakup and freeze up, local weather conditions, and large-scale climate oscillations from 1981–2015 for seven lakes in northern Wisconsin, USA, and two lakes in Ontario, Canada. Multiple linear regression models were developed to understand the drivers of lake ice phenology. We used projected air temperature and precipitation from 126 climate change scenarios to forecast the day of year of ice breakup and freeze up in 2050 and 2070. Lake ice melted 5 days earlier and froze 8 days later over the past 35 years. Warmer spring and winter air temperatures contributed to earlier ice breakup; whereas warmer November temperatures delayed lake freeze. Lake ice breakup is projected to be 13 days earlier on average by 2070, but could vary by 3 days later to 43 days earlier depending upon the degree of climatic warming by late century. Similarly, the timing of lake freeze up is projected to be delayed by 11 days on average by 2070, but could be 1 to 28 days later. Shortened seasonality of ice cover by 24 days could increase risk of algal blooms, reduce habitat for coldwater fisheries, and jeopardize survival of northern communities reliant on ice roads.

ЗМІНА ПРИЗЕМНОЇ ТЕМПЕРАТУРИ ПОВІТРЯ У ХХ – ХХІ СТОРІЧЧЯХ В РАЙОНІ АНТАРКТИЧНОГО ПІВОСТРОВА ЗА ДАНИМИ КЛІМАТИЧНИХ МОДЕЛЕЙ

This paper is dedicated to surface air temperature dynamics in the Antarctic region where the most rapid warming on the Earth was recorded during the second half of the 20th century. As air temperature is one of the main characteristics of climate system and climate model is the only tool to forecast future climate change, temperature data of Atmospheric-Ocean Global Circulation Models (AOGCMs) and measurements at the Ukrainian Antarctic Akademik Vernadsky station (former British Faraday station) were used for this research. Therefore, the main objectives of the research were to verify of 10 AOGCMs and their ensemble in comparison with observational data in period 1947-2016 at the station; to extract and to analyze climate projections till the end of the 21st century for the Antarctic Peninsula region based on scenarios of the 4th (SRES) and 5th (RCP) Reports of the Intergovernmental Panel on Climate Change (IPCC). Overall 93 model runs of 10 AOGCMs were analyzed. Comparative analysis between SRES and RCP IPCC scenarios of air temperature change has been performed and demonstrated, as a result, an absence of strong contradiction between the respective scenarios in surface air temperature projections both on global scale and in the Antarctic region. The verification of 10 AOGCMs runs has been carried out with 70-year observational data from Ukrainian Antarctic Akademik Vernadsky station where temperature has been increasing with rate of 0.51°С/10 years. Similar coefficients of linear trend and high correlation between measured air temperature at the station and in AOGCMs have confirmed that these climate models can be used for obtaining air temperature projections in the future. As a conclusion, analysis of climate projections for 21st century of 10 AOGCMs and their ensembles has demonstrated further temperature increase for all the above scenarios. The most rapid warming in the studied region has been expected for “pessimistic” A2 scenario with average temperature rise 0.29°С/10 years, “balanced” A1B scenario has projected 0.26°C/10 years and “optimistic” B1 – 0.15°C/10 years.

Electricity demand planning forecasts should consider climate non-stationarity to maintain reserve margins during heat waves

Climate non-stationarity is a challenge for electric power infrastructure reliability; recordbreaking heat waves significantly affect peak demand [1], lower contingency capacities, and expose cities to risk of blackouts due to component failures and security threats. The United States’ electric grid operates safely for a wide range of load, weather, and power quality conditions. Projected increases in ambient air temperatures could, however, create operating conditions that place the grid outside the boundaries of current reliability tolerances. Advancements in long-term forecasting, including projections of rising air temperatures and more severe heat waves, present opportunities to advance risk management methods for long-term infrastructure planning. This is particularly evident in the US Southwest—a relatively hot region expected to experience significant temperature increases affecting electric loads, generation, and delivery systems. Generation capacity is typically built to meet the 90th percentile (T90) hottest peak demand, plus an additional reserve margin of least 15%, but that may not be sufficient to ensure reliable power services if air temperatures are higher than expected. The problem with this T90 planning approach is that it requires a stationary climate to be completely effective. In reality, annual temperature differences can have more than a 15% effect on system performance. Current long-term infrastructure planning and risk management processes are biased climate data choices that can significantly underestimate peak demand, overestimate generation capacity, and result in major power outages during heat waves.This study used downscaled global climate models (GCMs) to evaluate the effects of non-stationarity on air temperature forecasts, and a new high-level statistical approach was developed to consider the subsequent effects on peak demand, power generation, and local reserve margins (LRMs) compared to previous forecasting methods. Air temperature projections in IPCC RCPs 4.5 and 8.5 are that increases up to 6°C are possible by the end of century, with highs of 58°C and 56°C in Phoenix, Arizona and Los Angeles, California respectively. In the hottest scenarios, we estimated that LRMs for the two metro regions would be on average 30% less than at respective T90s, which in the case of Los Angeles (a net importer) would require 5GW of additional power to meet electrical demand. We calculated these values by creating a structural equation model (SEM) for peak demand based on the physics of common AC units; physics-based models are necessary to predict demand under unprecedented conditions for which historical data do not exist. The SEM forecasts for peak demand were close to straight-line regression methods as in prior literature from 25–40°C (104°F), but diverged lower at higher temperatures. Power plant generation capacity derating factors were also modeled based on the electrical and thermal performance characteristics of different technologies. Lastly, we discussed several strategic options to reduce the risk of LRM shortages; including implementing technology, market incentives, and urban forms that reduce peak load and load variance per capita as well as their tradeoffs with several other stakeholder objectives.

Potential Salinity and Temperature Futures for the Chesapeake Bay Using a Statistical Downscaling Spatial Disaggregation Framework

Estuaries are productive and ecologically important ecosystems, incorporating environmental drivers from watersheds, rivers, and the coastal ocean. Climate change has potential to modify the physical properties of estuaries, with impacts on resident organisms. However, projections from general circulation models (GCMs) are generally too coarse to resolve important estuarine processes. Here, we statistically downscaled near-surface air temperature and precipitation projections to the scale of the Chesapeake Bay watershed and estuary. These variables were linked to Susquehanna River streamflow using a water balance model and finally to spatially resolved Chesapeake Bay surface temperature and salinity using statistical model trees. The low computational cost of this approach allowed rapid assessment of projected changes from four GCMs spanning a range of potential futures under a high CO2 emission scenario, for four different downscaling methods. Choice of GCM contributed strongly to the spread in projections, but choice of downscaling method was also influential in the warmest models. Models projected a ~2–5.5 °C increase in surface water temperatures in the Chesapeake Bay by the end of the century. Projections of salinity were more uncertain and spatially complex. Models showing increases in winter-spring streamflow generated freshening in the Upper Bay and tributaries, while models with decreased streamflow produced salinity increases. Changes to the Chesapeake Bay environment have implications for fish and invertebrate habitats, as well as migration, spawning phenology, recruitment, and occurrence of pathogens. Our results underline a potentially expanded role of statistical downscaling to complement dynamical approaches in assessing climate change impacts in dynamically challenging estuaries.