Future Climate Information Research Articles

A learning framework for designing climate services for capacity building

Climate services aim to provide climate information to aid decision-making. However, in practice their effectiveness is hampered by factors such as incompatibility with decisions, neglect of user’s context, and limited understanding of the information provided. Enhancing climate services’ user orientation necessitates capacity building to bolster the knowledge and collaborative skills among actors in the climate production chain. This study introduces a learning framework for capacity building in climate services that was developed using iterative systematic cycles for educational design research. The framework contains seven steps, along with relevant guiding questions, derived from the evaluation of case studies and the trainer’s experiences in 21 training events conducted under the User Learning Services (ULS) of the Copernicus Climate Change (C3S). Overall, this study offers a learning framework that can guide the content and design of capacity building in future climate information services. This contributes significantly to the ongoing effort to enhance and understand the impact of climate services on decision making.

Precipitation Projection in Cambodia Using Statistically Downscaled CMIP6 Models

The consequences of climate change are arising in the form of many types of natural disasters, such as flooding, drought, and tropical cyclones. Responding to climate change is a long horizontal run action that requires adaptation and mitigation strategies. Hence, future climate information is essential for developing effective strategies. This study explored the applicability of a statistical downscaling method, Bias-Corrected Spatial Disaggregation (BCSD), in downscaling climate models from the Coupled Model Intercomparison Project Phase 6 (CMIP6) and then applied the downscaled data to project the future condition of precipitation pattern and extreme events in Cambodia. We calculated four climate change indicators, namely mean precipitation changes, consecutive dry days (CDD), consecutive wet days (CWD), and maximum one-day precipitation (rx1day) under two shared socioeconomic pathways (SSPs) scenarios, which are SSP245 and SSP585. The results indicated the satisfactory performance of the BCSD method in capturing the spatial feature of orographic precipitation in Cambodia. The analysis of downscaled CMIP6 models shows that the mean precipitation in Cambodia increases during the wet season and slightly decreases in the dry season, and thus, there is a slight increase in annual rainfall. The projection of extreme climate indices shows that the CDD would likely increase under both climate change scenarios, indicating the potential threat of dry spells or drought events in Cambodia. In addition, CWD would likely increase under the SSP245 scenario and strongly decrease in the eastern part of the country under the SSP585 scenario, which inferred that the wet spell would have happened under the moderate scenario of climate change, but it would be the opposite under the SSP585 scenario. Moreover, rx1day would likely increase over most parts of Cambodia, especially under the SSP585 scenario at the end of the century. This can be inferred as a potential threat to extreme rainfall triggering flood events in the country due to climate change.

Climate Potential for Apple Growing in Norway—Part 1: Zoning of Areas with Heat Conditions Favorable for Apple Growing under Observed Climate Change

Agricultural production is already, and obviously, affected by climate change. Adapting to climate change includes reducing future risks to ensure yield quality and quantity and considers seizing any potential opportunities induced by climate change. In higher latitude areas, such as Norway, cold climate limits the cultivation of fruits. An increase in temperature offers more favorable conditions for fruit production. In this study, using available phenological observations (full blooming) and harvest dates, and meteorological data from the experimental orchard of NIBIO Ullensvang, the minimum heat requirements for growing different apple varieties are determined. Those criteria are used for zoning of the areas with heat favorable conditions for apple growing. Data on six varieties were used, with lower and higher requirements for heat for fruit development (Discovery, Gravenstein, Summerred, Aroma, Rubinstep, and Elstar). High resolution daily temperature data were generated and used for zoning of the areas with heat favorable conditions for apple growing within the selected domain, which includes Western Norway, Southern Norway, Eastern Norway, and the western part of Trøndelag, Mid-Norway. Dynamics of the change in such surfaces was assessed for the period of 1961–2020. The total surface with favorable heat conditions for growing the varieties with lesser requirement for heat increased three times during this period. The growing of more heat-demanding varieties increased from near zero to about 2.5% of the studied land surface. In the period of 2011–2020, surface area with favorable heat conditions for apple growing was almost 27,000 km2, and a surface area of about 4600 km2 can sustain growing of more heat-demanding varieties. The presented results show the increasing potential of the climate of Norway for apple cultivation and highlight the importance of implementation of fruit production planned according to climate change trends, including the assessment of potential risks from climate hazards. However, the methodology for determining heat requirements can be improved by using phenological ripening dates if available, rather than harvest dates which are impacted by human decision. Zoning of areas with the potential of sustainable apple growing requires the use of future climate change assessments and information on land-related features.

Gray wolf optimization-based wind power load mid-long term forecasting algorithm

Enhancing the utilization of new energy consumption relies significantly on mid-to-long-term load forecasting. However, current studies face limitations in effectively extracting and utilizing future climate forecast information. So, in this paper, we proposed a wind power mid-long-term load forecasting method considering different wind energy characteristics for effective future climate information prediction. Further, the wind energy resources climate forecast results data is taken as input to improve the adaptability of the prediction model and realize the screening of different forecast error characteristic data sets by building a wind energy feature mining model. Then, an adaptive prediction model is constructed based on the Gray wolf optimization algorithm (GWO) and the long short-term memory network (LSTM). The result obtained by the proposed model is compared with the existing research. From, the result analysis it is clear that the performance of the proposed prediction method is better than the existing research.

Climate Services for Agriculture: Tools for Informing Decisions Relating to Climate Change and Climate Variability in the Wine Industry

Background and Aims. Australia’s changing climate is already impacting the agriculture sector and will continue to do so in the future. To help respond to these impacts, the Climate Services for Agriculture (CSA) platform presents readily accessible climate data, including future climate projections, relevant to specific agricultural commodities. This wine industry example aims to demonstrate the functionality and utility of the CSA for national use across a broad range of commodities. Methods and Results. The platform includes commodity-relevant climate indices designed in consultation with experts to ensure that they are as salient to producers as possible; the wine-grape specific indices include measures of growing season temperature, rainfall, extreme heat, and frost. Here, we describe the research behind the wine-grape specific indices and present sample outputs from the CSA platform for a site within a selected winegrowing region. We note the CSA platform has been developed through an extensive and continuing user engagement initiative, ensuring it meets the needs of the agriculture community as they grapple with how to make decisions based on longer term climate projections. Conclusions. Provision of past, seasonal outlook, and future climate information for Australia and for a range of important agricultural commodities can help improve on-farm planning and decision-making to respond to climate risks. The wine industry provides a leading example of how to use these data for decision-making, noting ongoing adjustments will be needed. Significance of the Study. The CSA platform brings together historical climate data, seasonal climate outlooks, and future climate projections to assist agricultural producers to better manage climate variability and climate change. It aims to nationalise this information for all major agricultural commodities in Australia. We use wine production as a demonstration case here.

Fine-Scale Climate Projections: What Additional Fixed Spatial Detail Is Provided by a Convection-Permitting Model?

Abstract Convection-permitting (CP) models promise much in response to the demand for increased localization of future climate information: greater resolution of influential land surface characteristics, improved representation of convective storms, and unprecedented resolution of user-relevant data. In practice, however, it is contended that the benefits of enhanced resolution cannot be fully realized due to the gap between models’ computational and effective resolution. Nevertheless, where surface forcing is strongly heterogeneous, one can argue that usable information may persist close to the grid scale. Here we analyze a 4.5-km resolution CP projection for Africa, asking whether and where fine-scale projection detail is robust at sub-25-km scales, focusing on geolocated rainfall features (rather than Lagrangian motion). Statistically significant detail for seasonal means and daily extremes is most frequent in regions of high topographic variability, most prominently in East Africa throughout the annual cycle, West Africa in the monsoon season, and to a lesser extent over Southern Africa. Lake coastal features have smaller but significant impacts on projection detail, whereas ocean coastlines and urban conurbations have little or no detectable impact. The amplitude of this sub-25-km projection detail can be similar to that of the local climatology in mountainous regions (or around a third near East Africa’s lake shores), so potentially beneficial for improved localization of future climate information. In flatter regions distant from coasts (the majority of Africa), spatial heterogeneity can be explained by chaotic weather variability. Here, the robustness of local climate projection information can be substantially enhanced by spatial aggregation to approximately 25-km scales, especially for daily extremes and equatorial regions. Significance Statement Recent substantial increases in the horizontal resolution of climate models bring the potential for both more reliable and more local future climate information. However, the best spatial scale on which to analyze such data for impacts assessments remains unclear. We examine a 4.5-km resolution climate projection for Africa, focusing on seasonal and daily rainfall. Spatially fixed fine-scale projection detail is found to be statistically robust at sub-25-km scales in the most mountainous regions and to a lesser extent along lake coastlines. Elsewhere, the model data may be better aggregated to at least 25-km scales to reduce sampling uncertainties. Such evolving guidance on the circumstances and extent of high-resolution data aggregation will help users gain greater benefit from climate model projections.

Incorporating uncertainty from downscaled rainfall projections into climate resilience planning in U.S. cities

The planning, design, and maintenance of stormwater infrastructure must be informed by changing rainfall patterns due to climate change. However, there is little consensus on how future climate information should be used, or how uncertainties introduced by use of different methods and datasets should be characterized or managed. These uncertainties exacerbate existing challenges to using climate information on local or municipal scales. Here we analyze major cities in the U.S., 48 of which developed climate adaptation and resilience plans. Given the prevalence of depth duration frequency (DDF) curves for planning infrastructure for rainfall, we then assessed the underlying climate information used in these 48 plans to show how DDF curves used for resilience planning and the resulting outcomes can be affected by stakeholders’ methodological choices and datasets. For rainfall extremes, many resilience plans varied by trend detection method, data preprocessing steps, and size of study area, and all used only one of the available downscaled climate projection datasets. We evaluate the implications of uncertainties across five available climate datasets and show the level of climate resilience to extreme rainfall depends on the dataset selected for each city. We produce risk matrices for a broader set of 77 U.S. cities to highlight how local resilience strategies and decisions are sensitive to the climate projection dataset used in local adaptation plans. To help overcome barriers to using climate information, we provide an open dataset of future daily rainfall values for 2-, 5-, 10-, 25-, 50-, and 100 years annual recurrence intervals for 77 cities and compare resilience outcomes across available climate datasets that each city can use for comparison and for robust resilience planning. Because of uncertainty in climate projections, our results highlight the importance of no-regret and flexible resilience strategies that can be adjusted with new climate information.

A framework of integrating heterogeneous data sources for monthly streamflow prediction using a state-of-the-art deep learning model

Deep learning has been widely used in hydrological prediction such as monthly streamflow and its performance is usually dependent on the abundance of training data. Even though the interest in using predictors from multiple data sources (e.g., streamflow observations, local meteorological data, and large-scale climate indexes) to train deep learning models for monthly streamflow prediction is growing, these predictors are usually selected from historical periods. Such approaches have limitations that the non-stationary future climate information is not included in the deep learning models. Climate models can provide non-stationary climate information for the future period, which may be useful for monthly streamflow prediction. Therefore, the objectives of this study are to (1) investigate the added value of using predictors derived from global climate models (GCMs) for monthly streamflow prediction based on a state-of-the-art deep learning model, and (2) propose a framework for integrating heterogeneous data sources for monthly streamflow prediction. The framework consists of five integral components: data collection, predictor combination, predictor selection, model construction, and results evaluation. In this study, a hybrid deep learning model combining Convolutional Neural Network and Gated Recurrent Unit was applied for six hydrological stations from mainstream and six stations from tributaries of the Yangtze River. Historical hydroclimate data and GCM hindcasts from 1982 to 2010 are used in monthly streamflow forecasts. Hindcasts are retrospective forecasts for many variables in the past, ideally conducted using the same model used for real-time forecasts. The results show that GCM hindcasts are useful predictors to improve the prediction accuracy for monthly streamflow predictions, especially for the 1- and 3-month lead times. Combining GCM hindcasts with either historical meteorological data or historical streamflow observations and meteorological data as predictors generally provides the best predictive performance. In addition, using large-scale climate indexes as ancillary information is able to improve the predictive performance at a lead time of 6 months. For lead times of 1, 3, and 6 months, the Kling‐Gupta efficiency (KGE) and the mean relative error (MRE)) metrics calculated based on the best-performing predictor combinations are satisfactory for hydrological stations in both mainstream and tributaries, with the median KGE being higher than 0.85 and 0.62, and the median MRE being approximately 20 % and 40 %, respectively, suggesting the monthly streamflow predictions are better for mainstream than for tributaries. Overall results show that (1) the inclusion of predicted information from GCMs can improve the performance for monthly streamflow prediction, and (2) the way of combining various heterogeneous data sources is crucial.

제주 지역별 기후변화 특성과 건축물 에너지 요구량에 관한 연구

The purpose of this study is to investigate the climate change of the past 30 years in Jeju Island and to analyze the change in energy demand accordingly. As a research method, data from the four regions (Jeju-si, Gosan, Seongsan, Seogwipo) of the Korea Meteorological Administration (ASOS) were used as climate data, and the amount of cooling and heating energy required was calculated through Energy # v2.5, an energy analysis program. The resulting values were derived. As for climate change by region, the average temperature in the middle and summer continued to rise compared to the winter, and the average relative humidity also increased in the summer. As a result of analyzing the change in energy demand by region (1991 ~ 2020), it was found that cooling energy demand increased due to the increase in average temperature and relative humidity in all four regions. In addition, due to the increase in average temperature, the heating energy demand in winter is showing a tendency to decrease slightly. In particular, Jeju, Gosan, and Seongsan regions show a higher change than Seogwipo regions. Through the analysis of climate information and energy demand for the four regions of Jeju, it will be possible to predict the change in the building energy performance of Jeju Island by estimating the trend of climate change over the past 30 years, the change in energy consumption, and future climate information.

Cost-Risk Analysis Reconsidered—Value of Information on the Climate Sensitivity in the Integrated Assessment Model PRICE

Infeasible solutions or negative expected values of future climate information are undesired problems if climate policies are adopted under Cost-Effectiveness Analysis (CEA) to reach uncertain temperature targets. Cost-Risk Analysis (CRA) was developed to resolve these issues. It allows for a trade-off between expected welfare losses of mitigation and avoided risk of transgressing a climate target with a certain probability of compliance (Safety). Some of the significant contributions of this paper are: (i) It updates the Probabilistic Integrated model of Climate and the Economy (PRICE) as a probabilistic version of the latest version of the Dynamic Integrated Climate-Economy model (DICE) 2016, and it extends the model to run welfare-maximizing decision analytic frameworks readily. (ii) It highlights that the standard method of applying CRA (Old CRA) leads to an extra welfare cost. (iii) It proposes revised instruction on how to use CRA. (iv) It simulates and compares welfare-maximizing decision analytic frameworks on the level of risk, damages, and carbon prices. (v) It measures the value of information using risk-based methods and compares them with the value of information calculated using the damage-based method. (vi) It measures the carbon prices for the CRA scenarios for the first time. The results show that the choice of the disutility function governs the magnitude of the value of information. Using a damage function or Old CRA, the value of information is significantly high for new information arriving between 2020 and 2060. If the New CRA is applied, however, such benefits are negligible.

Using expert elicitation to strengthen future regional climate information for climate services

Climate change knowledge can inform regional and local adaptation decisions. However, estimates of future climate are uncertain and methods for assessing uncertainties typically rely on the results of climate model simulations, which are constrained by the quality of assumptions used in model experiments and the limitations of available models. To strengthen scientific knowledge for climate services and climate change adaptation decisions, we explore the use of structured expert elicitation to assess future regional climate change. Using the Lower Yangtze region in China as a case study, we elicit judgements from six experts on future changes in temperature and precipitation as well as uncertainty sources, and compare it with climate model outputs from the Couple Model Intercomparison Project phase 5 (CMIP5). We find high consensus amongst experts that the Lower Yangtze region will be warmer in the coming decades, albeit with differences in the magnitude of change. There is less consensus about the direction and magnitude of future precipitation change. Compared with CMIP5 climate model outputs, experts provide similar or narrower uncertainty ranges for temperature change and very different uncertainty ranges for precipitation. Experts considered additional factors (e.g. model credibility, observations, theory and paleo-climatic evidence) and uncertainties not usually represented in conventional modelling approaches. We argue that, in context of regional climate information provision, expert-elicited judgements can characterise less predictable, or less explored, elements of the climate system and expert-elicited reasoning provides additional information and knowledge that is absent from modelling approaches. We discuss the value in bringing together multiple lines of evidence, arguing that expert elicited information can complement model information to strengthen regional climate change knowledge and help in building dialogue between climate experts and regional stakeholders, as part of a more complete climate service.

Toward usable predictive climate information at decadal timescales

Toward usable predictive climate information at decadal timescales

Future extreme heat wave events using Bayesian heat wave intensity-persistence day-frequency model and their uncertainty

Heat wave events are occurring more often and over a wider area around the world, and in the coming era of global warming, their intensity will intensify and their persistence days will be longer. In this study, the relationship between the occurrence probability, intensity, and persistency days of future extreme heat wave events is explained using the concept of IDF (rainfall intensity-duration-frequency) curves for extreme rainfall events. The uncertainty of the non-stationary heat wave intensity-persistence day-frequency (HPF) model is analyzed using Bayesian inference. Non-stationary HPF curves were applied to a total of 16 future daily maximum surface air temperature ensembles projected at six major sites in Korea. From the ensemble of future climate information, it was found that the intensity of the extreme heat wave event for persistence day of 2-day estimated in 2050 would be more likely to rise in the range of 1.23–1.69 °C (under RCP 4.5) and 1.15–1.96 °C (under RCP 8.5) than that estimated in 2010. It was also found that the uncertainty resulting from parameter estimation of the HPF model was greater than the uncertainty resulting from the inter-model variability of various climate model combinations. When reflecting the uncertainty resulting from the estimation of model parameters, the 95% confidence interval of the delta change for the heat wave intensity projected in 2050 from 2010 was estimated to be 0.53–4.94 °C (under RCP 4.5) and 0.89–5.57 °C (under RCP 8.5).

Tailored climate projections to assess site-specific vulnerability of tea production

Tailored climate change information is essential to understand future climate risks and identify relevant adaptation strategies. However, distilling and effectively communicating decision-relevant information from climate science remains challenging. In this paper, we develop and apply an iterative stakeholder engagement approach and a Site Specific Synthesis of Projected Range (SPR), to co-produce future climate information for Africa’s largest tea producing nations - Kenya and Malawi - for the mid-and late-21st century. SPR provides a novel analysis approach, which combines long-term station observations with projections from 29 global climate models and the first convection-permitting high-resolution climate projection for Africa (CP4A). This addresses the mismatch between spatial scales of projections, large-scale modelling uncertainties and stakeholder need for site-specific information. Iterative stakeholder engagement and communication helped to build trust, allowed use of new observation data and improved visualisations of climate information for stakeholders. SPR demonstrates site-specificity in changes in all metrics, showing risks of large changes in tea crop sensitive metrics. All nine locations analysed show substantial (up to four times) increases in heatwave days and large decreases in cold nights by 2050s compared to the current climate. While tea producers are already witnessing changing climatic conditions, potential future changes will greatly affect the resilience of tea production, thereby affecting the sustainability and quality of tea production in the region. Site specific climate information iteratively co-produced with stakeholders helps them to identify location-specific adaptation strategies and investment priorities, potentially safeguarding supply-chains and millions of livelihoods.

Drought Trends and the Extreme Drought Frequency and Characteristics under Climate Change Based on SPI and HI in the Upper and Middle Reaches of the Huai River Basin, China

The Huai River Basin (HRB) is an important grain and industrial production area in eastern China with frequent droughts. Under the background of current climate change, the hydrological and meteorological characteristics of the basin may be changed, which may lead to the changes of regional drought characteristics. It should be paid more attention on the drought research under climate change and the difference between different drought indices. Coupled Model Intercomparison Project phase 5 (CMIP5) as an important tool for climate change research has been used in the study and the study chosen three global circulation models (GCMs)—such as CNRM-CM5 (CNR), HadGEM2-ES (Had), and MIROC5 (MIR)—to gather an ensemble model (EnM) for providing the future climate information. The Standardized Precipitation Index (SPI) and Humidity Index (HI) were used to evaluate and compare the drought situations in the past and the future periods with two representative concentration path scenarios (RCP4.5 & RCP8.5). Some sequence statistics methods, such as Mann–Kendall test and run theory, were carried out to analyze the trend and the changes of extreme drought frequency and characteristics values. The research showed that the simulation accuracy of the EnM would better. SPI and HI take different factors into count and thus lead to differences in describing drought trend, extreme drought frequency, and characteristic values, such as drought severity, drought duration, mean drought severity, and max drought intensity. The research showed that both SPI and HI showed the same wetting or drying trend in the same timescales (except winter) as in the historical data. However, the future annual and seasonal drought trend reflected by SPI shows a wetting trend while HI shows a drying trend. Both in the past and the future, extreme drought frequency and characteristic values reflected by HI are higher than SPI. The drought trend is greater, and the extreme drought frequency and characteristics tend to be strengthened under RCP8.5. Low precipitation and high potential evapotranspiration (PET), especially the PET caused by temperature rise, are the main influencing factors of drought in the future. Therefore, the influence of the PET should not be ignored in drought analysis and we should strengthen the comparative study of different drought indices in future drought analysis under climate change.

The Potential Global Distribution of the White Peach Scale Pseudaulacaspis pentagona (Targioni Tozzetti) under Climate Change

The white peach scale Pseudaulacaspis pentagona (Hemiptera: Diaspididae) is a pest that causes significant damage to more than 221 genera of host plants in more than 112 countries. P. pentagona primarily feeds on mulberry, peach, and tea, and this leads to the loosening of the epidermis of trees, which damages nutrient and water transportation in the branches, leading to branch death. P. pentagona is native to China and Japan, and has become an invasive species all over the world. However, the potential distribution of P. pentagona remains unclear. In this study, a potential distribution map of P. pentagona was developed using current and future climate information using MaxEnt. The model indicates that Asia, Europe, South America and North America are a highly suitable habitat range for this species. The MaxEnt models for the potential distribution of P. pentagona for the 2050s and 2070s suggest that in the case of no significant increase or even decrease in the highly suitable area, the suitable area increased significantly on any future climatic scenarios. The predicted area gain in the suitable habitat is 2.82 × 107 km2, including more of Asia, such as China, Japan, and Mongolia, and also including India, Vietnam, Romania, Ukraine, Poland, Hungary, Austria, The Czech Republic, Italy, and Germany in Europe, which shows an increase of 24.5% over the current habitat on RCP8.5 emission scenarios for the 2070s. With the warming of the climate, significant expansions are predicted in the suitable area, especially in Europe and East Asia. Under RCP8.5 for the 2050s, the model-predicted that the area of suitable habitat in China and the Korean Peninsula gains an increase of 18.8% over the current suitable habitat area. Under other climate scenarios, RCP8.5-2070s, the suitable areas were the largest, compared to projection for the current climate scenario (ca. 24.1% increase) which increased to 7.89 × 106 km2. In Europe, under RCP8.5 for the 2070s, the highly suitable areas were the largest, compared to the projection for the current climate scenario (ca. 46.2% increase), which increased to 8.64 × 105 km2, the area of suitable habitat suitability increased to 4.99 × 106 km2 (29.2% increase of the current condition). Potential increases or decreases in distribution ranges were modeled under future climatic scenarios. This study suggests that the most important factor that influenced current distribution of this pest was temperature, and BIO3 (isothermality) was the most important factor that contributed to 48.6% of the potential distribution map. Given the rapid spread of P. pentagona and the serious risk this species poses to local ecosystems, warning modelling and practical strategies to prevent the establishment and expansion of this species should be sought. This distribution map will help governments to identify areas that are suitable for current and future infestations, and to optimize pest management strategies.

Stochastic Parsimonious Hydrologic Partitioning Model under East Asia Monsoon Climate and Its Application to Climate Change

A conceptual hydrologic partitioning model suitable for the East Asia monsoon climate region is constructed parsimoniously, and the variability of Horton index, which is the ratio of water vaporization and wetting in the watershed, is investigated. Numerical simulations in the study area show that the inter-annual variability of Horton index is reduced to around 60% of the inter-annual variability of annual precipitation, and there is a strong inverse correlation between Horton index and annual precipitation. Using cumulant expansion theory, the probability distribution function of soil water with various hydro-meteorological variables and watershed characteristics is derived. Using the steady-state soil water probability distribution function, the sensitivity of Horton index to hydro-meteorological variables such as precipitation occurrence probability, average rainfall depth at rainy days, and evapotranspiration rate and hydro-geophysical characteristics such as surface runoff coefficients, threshold soil water value to control vaporization, and exponent value to control groundwater recharge is analyzed. Looking at the future Horton index of the study area using a variety of future climate information ensemble, it is projected that the water stress of vegetation in the watershed is likely to increase due to fluctuations in precipitation patterns and increase in potential evapotranspiration even if annual precipitation increases.

The effects of climate change on crop yields using RCP scenarios with SWAT agro-hydrological model in Maharlu Basin (Fars Province - Iran)

Climate change is one of the most important issues that have been raised in water sources and agriculture limiting the crop production. In this study, the northern part of the Maharlu Lake in the central plateau of Iran is simulated using a semi-distributed SWAT model during 1980-2013. Then, using the LARS-WG, future climate information is obtained based on RCP 4.5 and RCP8.5 scenarios. Furthermore, assessment of the effects of climate change showed that the average annual temperature of the basin in the future period (2021-2040) will increase about 1.5°C and 1.8°C for the RCP4.5 and RCP8.5 scenarios, respectively compared with the baseline period. Simulations and the calculation of the long-term average of temperature and precipitation, and the model combination were used to estimate crop yields using the semidistributed SWAT model. NSE showed acceptable range of SWAT model accuracy (~0.65) .Results of crop yields showed that in future wheat yield will increase from 3.4 ton/ha to 4 and 4.4 ton/ha. In addition, almond, tomato, apple, and olive will face decreased yields.

Dynamical downscaling of regional climate: A review of methods and limitations

The traditional dynamical downscaling (TDD) method employs continuous integration of regional climate models (RCM) with the general circulation model (GCM) providing the initial and lateral boundary conditions. Dynamical downscaling simulations are constrained by physical principles and can generate a full set of climate information, providing one of the important approaches to projecting fine spatial-scale future climate information. However, the systematic biases of climate models often degrade the TDD simulations and hinder the application of dynamical downscaling in the climate-change related studies. New methods developed over past decades improve the performance of dynamical downscaling simulations. These methods can be divided into four groups: the TDD method, the pseudo global warming method, dynamical downscaling with GCM bias corrections, and dynamical downscaling with both GCM and RCM bias corrections. These dynamical downscaling methods are reviewed and compared in this paper. The merits and limitations of each dynamical downscaling method are also discussed. In addition, the challenges and potential directions in progressing dynamical downscaling methods are stated.

High-resolution climate projections for South Asia to inform climate impacts and adaptation studies in the Ganges-Brahmaputra-Meghna and Mahanadi deltas

Climate impacts and adaptation studies often use output from impact models that require data representing future climates at a resolution greater than can be provided by Global Climate Models (GCMs). This paper describes the use of Regional Climate Model (RCM) simulations to generate high-resolution future climate information for assessing climate impacts in the Ganges-Brahmaputra-Meghna (GBM) and Mahanadi deltas as part of the DECCMA project. In this study, three different GCMs (HadGEM2-ES, CNRM-CM5 and GFDL-CM3), all using a single scenario for future greenhouse forcing of the atmosphere (RCP 8.5), were downscaled to a horizontal resolution of 25 km over south Asia using the HadRM3P RCM. These three GCMs were selected based on ability to represent key climate processes over south Asia and ability to sample a range of regional climate change responses to greenhouse gas forcing. RCM simulations of temperature, precipitation, and lower level (850 hPa) atmospheric circulation in the monsoon season (June, July, August, September – JJAS) were compared with observational datasets and their respective driving GCMs to ensure large-scale consistency. Although there are some biases in the RCM simulations, these comparisons indicate that the RCMs are able to simulate realistically aspects of the observed climate of South Asia, such as the monsoon circulation and associated precipitation that are key for informing downstream impacts and adaptation studies. Simulated future temperature and precipitation changes on seasonal and daily timescales suggest increases in both temperature and precipitation across all three models during the monsoon season, with an increase in the amount of extremely heavy precipitation over the GBM and Mahanadi basins. Despite different driving conditions, these results are consistent across all three RCM simulations, providing a level of confidence in the magnitudes and spatial characteristics of temperature and precipitation projections for South Asia.