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Egypt, a predominantly arid and hyper-arid country, is one of the environmentally most fragile regions of the world. The country became a hot spot for climatic extremes and aridity change in the global warming context. The unavailability of a detailed and reliable climate zonation map is a major hindrance to climatic studies in Egypt. This study attempted to generate a high-resolution climate zone map of Egypt based on a novel image analysis technique. For this purpose, a colored image representing Egypt's composite climatology was developed using three high-resolution (1-km) climate variables: rainfall, maximum temperature and minimum temperature during 1979–2013. A spherical evolution algorithm was used to classify the image into different climate zones. Subsequently, the climate zones representing similar climate distribution were merged to generate the climate map of Egypt. The study revealed that Egypt's distinguishable climate zones could be recognized when the land area was classified into nine zones using the image analysis technique. The statistical analysis of climate variables of each zone revealed similar climatology only in two pairs of zones. The merging of similar climate zones yielded seven climate zones having distinct climate characteristics. The validation of climate zonation using various statistical tests revealed the robustness of the proposed method in classifying climate. The climate zone map generated in the study can be used as a reference for climate change analysis in Egypt. [ABSTRACT FROM AUTHOR]

We present results of an effort to evaluate the ability of an analytic model to describe the behavior of the equatorial zonal plasma drifts given inputs provided by readily available climatological models of thermospheric and ionospheric parameters. In a data-model fusion approach, we used vertical drift measurements to drive the model and zonal drift measurements to evaluate its output. Drift measurements were made by the Jicamarca incoherent scatter radar, and model results were evaluated for different seasons and two distinct solar flux conditions. We focused, in particular, on model results for different versions of the Horizontal Wind Model (HWM 97, 07, and 14). We found that, despite its simplicity, the analytic model can reproduce fairly well most of the features in the observed zonal plasma drifts, including the vertical shear associated with the evening plasma vortex. During daytime hours the model predicts similar results for the zonal drifts independently of the HWM used to drive the model. More importantly, the modeled daytime drifts match exceptionally well the behavior and magnitude of the observed drifts for all seasons and solar flux conditions considered. The nighttime results drive the overall performance of the analytic model, and we found that a single HWM cannot provide the best results for all seasons and solar flux conditions. We also examined the main sources of zonal drift variability. Most of the morphology is controlled by the zonal wind dynamo term of the analytic model, but with non-negligible contribution from the vertical drift term. Finally, we examined the contribution from the E- and F-region to the zonal wind dynamo. The morphology of the zonal drifts in the region of observation (240–560 km altitude) is controlled mostly by the F-region winds, but with significant contributions from the daytime E-region particularly during December solstice and low solar flux conditions. [ABSTRACT FROM AUTHOR]

Ensembles of climate change projections created by general circulation models (GCMs) with high resolution are increasingly needed to develop adaptation strategies for regional climate change. The Meteorological Research Institute atmospheric GCM version 3.2 (MRI-AGCM3.2), which is listed in the Coupled Model Intercomparison Project phase 5 (CMIP5), has been typically run with resolutions of 60 km and 20 km. Ensembles of MRI-AGCM3.2 consist of members with multiple cumulus convection schemes and different patterns of future sea surface temperature, and are utilized together with their downscaled data; however, the limited size of the high-resolution ensemble may lead to undesirable biases and uncertainty in future climate projections that will limit its appropriateness and effectiveness for studies on climate change and impact assessments. In this study, to develop a comprehensive understanding of the regional precipitation simulated with MRI-AGCM3.2, we investigate how well MRI-AGCM3.2 simulates the present-day regional precipitation around the globe and compare the uncertainty in future precipitation changes and the change projection itself between MRI-AGCM3.2 and the CMIP5 multiple atmosphere–ocean coupled GCM (AOGCM) ensemble. MRI-AGCM3.2 reduces the bias of the regional mean precipitation obtained with the high-performing CMIP5 models, with a reduction of approximately 20% in the bias over the Tibetan Plateau through East Asia and Australia. When 26 global land regions are considered, MRI-AGCM3.2 simulates the spatial pattern and the regional mean realistically in more regions than the individual CMIP5 models. As for the future projections, in 20 of the 26 regions, the sign of annual precipitation change is identical between the 50th percentiles of the MRI-AGCM3.2 ensemble and the CMIP5 multi-model ensemble. In the other six regions around the tropical South Pacific, the differences in modeling with and without atmosphere–ocean coupling may affect the projections. The uncertainty in future changes in annual precipitation from MRI-AGCM3.2 partially overlaps the maximum–minimum uncertainty range from the full ensemble of the CMIP5 models in all regions. Moreover, on average over individual regions, the projections from MRI-AGCM3.2 spread over roughly 0.8 of the uncertainty range from the high-performing CMIP5 models compared to 0.4 of the range of the full ensemble. [ABSTRACT FROM AUTHOR]

This study presents the results of the ERTDF S-12 project for searching an optimum reduction scenario of the short-lived climate pollutants (SLCPs) to simultaneously mitigate the global warming and environmental problems. The study utilized REAS emission inventory, Asia-Pacific Integrated Model-Enduse (AIM/Enduse), MIROC6 climate model, NICAM non-hydrostatic atmospheric model, and models for estimating environmental damages to health, agriculture, and flood risks. Results of various scenario search indicate that it is difficult to attain simultaneous reduction of global warming and environmental damages, unless a significant reduction of CO2 is combined with carefully designed SLCP reductions for CH4, SO2, black carbon (BC), NOx, CO, and VOCs. In this scenario design, it is important to take into account the impact of small BC reduction to the surface air temperature and complex atmospheric chemical interactions such as negative feedback between CH4 and NOx reduction. We identified two scenarios, i.e., B2a and B1c scenarios which combine the 2D-scenario with SLCP mitigation measures using End-of-Pipe (EoP) and new mitigation technologies, as promising to simultaneously mitigate the temperature rise by about 0.33 °C by 2050 and air pollution in most of the globe for reducing damages in health, agriculture, and flood risk. In Asia and other heavy air pollution areas, health-care measures have to be enhanced in order to suppress the mortality increase due to high temperature in hot spot areas caused by a significant cut of particulate matter. For this situation, the B1b scenario is better to reduce hot spot areas and high-temperature damage to the public health. [ABSTRACT FROM AUTHOR]

A large-ensemble climate simulation database, which is known as the database for policy decision-making for future climate changes (d4PDF), was designed for climate change risk assessments. Since the completion of the first set of climate simulations in 2015, the database has been growing continuously. It contains the results of ensemble simulations conducted over a total of thousands years respectively for past and future climates using high-resolution global (60 km horizontal mesh) and regional (20 km mesh) atmospheric models. Several sets of future climate simulations are available, in which global mean surface air temperatures are forced to be higher by 4 K, 2 K, and 1.5 K relative to preindustrial levels. Nonwarming past climate simulations are incorporated in d4PDF along with the past climate simulations. The total data volume is approximately 2 petabytes. The atmospheric models satisfactorily simulate the past climate in terms of climatology, natural variations, and extreme events such as heavy precipitation and tropical cyclones. In addition, data users can obtain statistically significant changes in mean states or weather and climate extremes of interest between the past and future climates via a simple arithmetic computation without any statistical assumptions. The database is helpful in understanding future changes in climate states and in attributing past climate events to global warming. Impact assessment studies for climate changes have concurrently been performed in various research areas such as natural hazard, hydrology, civil engineering, agriculture, health, and insurance. The database has now become essential for promoting climate and risk assessment studies and for devising climate adaptation policies. Moreover, it has helped in establishing an interdisciplinary research community on global warming across Japan. [ABSTRACT FROM AUTHOR]

Earth system models (ESMs) consist of parameterization schemes based on one's perception of how the Earth system functions. A typical ESM contains a large number of parameters (i.e., the constants and exponents in the parameterization schemes) whose specification can have a significant impact on an ESM's simulation capabilities. Sensitivity analyses (SA) is an important tool for assessing how parameter specification influences model simulations. In this study, we used an Earth system model of intermediate complexity (EMIC)—LOVECLIM as an example to illustrate how SA methods can be used to identify the most sensitive parameters that control the simulations of several key global water and energy cycle variables, including global annual mean absolute surface air temperature (TG), precipitation and evaporation over the land and over the oceans (PL, PO, EL, EO), and land runoff (RL). We also demonstrate how judiciously specifying model parameters can improve the simulations of those variables. Three SA methods MARS, RF, and sparse PCE-based Sobol' method were used to evaluate a pool of 25 adjustable parameters chosen from land, atmosphere, and ocean components of LOVECLIM and their results were intercompared to ensure robustness of the results. It is found that with different parameter specification, TG can vary from 10 to 20 °C, and the values of PL, PO, EL, and EO can change by more than 100%. An interesting observation is that the value of RL vary from 13,000 to 35,000 km3, far below the observed climatological value of 40,000 km3, indicating a model structural deficiency in representing land runoff by LOVECLIM which must be corrected to obtain more reasonable global water budgets. We also note that parameter sensitivities are significantly different at different latitudes. Finally, we showed that global water and energy cycle simulations can be significantly improved by even a crude automatic parameter tuning, indicating that parameter optimization can be a viable way to improve ESM climate simulations. The results from this study should help us to understand the parameter uncertainty of a full-scale ESM. [ABSTRACT FROM AUTHOR]

During the Monsoon Asian Hydro−Atmosphere Scientific Research and Prediction Initiative (MAHASRI; 2006-16), we carried out two projects over the Indonesian maritime continent (IMC), constructing the Hydrometeorological Array for Intraseasonal Variation−Monsoon Automonitoring (HARIMAU; 2005-10) radar network and setting up a prototype institute for climate studies, the Maritime Continent Center of Excellence (MCCOE; 2009-14). Here, we review the climatological features of the world’s largest “regional” rainfall over the IMC studied in these projects. The fundamental mode of atmospheric variability over the IMC is the diurnal cycle generated along coastlines by land−sea temperature contrast: afternoon land becomes hotter than sea by clear-sky insolation before noon, with the opposite contrast before sunrise caused by evening rainfall-induced “sprinkler”-like land cooling (different from the extratropical infrared cooling on clear nights). Thus, unlike the extratropics, the diurnal cycle over the IMC is more important in the rainy season. The intraseasonal, seasonal to annual, and interannual climate variabilities appear as amplitude modulations of the diurnal cycle. For example, in Jawa and Bali the rainy season is the southern hemispheric summer, because land heating in the clear morning and water vapor transport by afternoon sea breeze is strongest in the season of maximum insolation. During El Niño, cooler sea water surrounding the IMC makes morning maritime convection and rainfall weaker than normal. Because the diurnal cycle is almost the only mechanism generating convective clouds systematically near the equator with little cyclone activity, the local annual rainfall amount in the tropics is a steeply decreasing function of coastal distance (e-folding scale 100-300 km), and regional annual rainfall is an increasing function of “coastline density” (coastal length/land area) measured at a horizontal resolution of 100 km. The coastline density effect explains why rainfall and latent heating over the IMC are twice the global mean for an area that makes up only 4% of the Earth’s surface. The diurnal cycles appearing almost synchronously over the whole IMC generate teleconnections between the IMC convection and the global climate. Thus, high-resolution (<< 100 km; << 1 day) observations and models over the IMC are essential to improve both local disaster prevention and global climate prediction. [ABSTRACT FROM AUTHOR]