Peasant time as textual time: reading the diaries of a Kashmiri peasant

This study uses daily rainfall data from 20 global climate models (GCMs) simulations, participating in the phase 5 of the Coupled Model Intercomparison Project (CMIP5) and eight daily rainfall indices defined by the Expert Team on Climate Change Detection and Indices (ETCCDI), to investigate the changes in extreme weather conditions over Central Africa under the representative concentration pathway 8.5. The performance of the multi-model ensemble (MME) mean which in fact refers to the best performing models selected through the Taylor diagram analysis was evaluated by comparing with two gridded daily observation datasets during the historical period (1998–2005). Results show that although some uncertainties may exist between the gridded observation datasets, MME consistently outperform individual models and reasonably reproduced the observed pattern of daily rainfall indices over the region, except in the case of consecutive wet day (CWD) where the high variability of individual members has resulted in the degradation of the overall skill of the MME. The assessment of the climate change signal in the eight daily rainfall indices was done for the mid and late twenty-first century (2026–2056 and 2066–2095 respectively), relative to the baseline historical time period (1976–2005). We found a significant increase in the total wet day rainfall amount (PRCPTOT) over southern (northern) Central Africa from December to February (from September to November). This is mainly due to the increase of high intense rainfall events rather than their frequency. The results also reveal that the increase in PRCPTOT was coupled with increase in the maximum consecutive 5-day rainfall amount (RX5DAY), the 95th percentile (R95), and the total wet day rainfall amount above the 95th percentile (R95PTOT), with more robust patterns of change at the late twenty-first century. The increase in extreme rainfall events (RX5DAY, R95, and R95PTOT) is likely to increase flood risks over Cameroon, Central African Republic, Gabon, Congo, Angola, Zambia, and Democratic Republic of Congo. On the other hand, changes in CWD and PRCPTOT are projected to significantly decrease over Angola, Zambia, and Democratic Republic of Congo from September to November. This is due to a substantial increase of zonal moisture divergence fluxes in upper atmospheric layers. The analysis has also shown that areas where CWD and PRCPTOT decreases coincides with those where consecutive dry days (CDD) increase. The decrease in CWD and PRCPTOT coupled with the increase in CDD could worsen drought risk and significantly disrupt priority socio-economic sectors for development such as rain-fed agriculture, hydroelectric power generation, and water resource availability. The results thus underline the importance for decision-makers to seriously consider adaptation and mitigation measures, in order to limit the risks of natural disasters such as severe droughts and floods that Central African countries may suffer in the future.

Models developed to capture underlying river processes over long historical periods and varying hydrologic conditions provide confidence for subsequent forecasting applications. However, many areas lack the weather data needed to develop process‐based models over these long periods. Climate reanalysis data sets (CRDs) are increasingly used as surrogates for historical meteorology, but their use in river temperature models is still relatively new and untested. Testing of temperature models using CRDs in rivers experiencing a range of instream flow, weather, and topographic conditions is needed to validate the application of these data sets. Focusing on the ERA5‐Land CRD, correction methods that relate weather variables and elevation were tested using weather stations surrounding and adjacent to the Colorado River in Grand Canyon. Our findings show that elevation corrections improved air temperature and relative humidity, but negatively impacted wind speed estimates. Two‐year river temperature model simulations in a 387‐km segment of the Colorado River in Grand Canyon and a 576‐km segment of the Green River showed that using elevation‐corrected ERA5‐Land inputs produced lower mean errors at downstream river locations when compared to predictions using elevation‐corrected ground‐based inputs. Better river temperature predictions when using ERA5‐Land are attributed to the ability to represent spatial variability in weather conditions over these large areas. These promising results persisted when spatially coarsened ERA5‐Land inputs were used. This study highlights the importance of having spatially varying weather information, even at relatively coarse resolutions, when modeling physical processes over large spatial scales and suggests confidence in using CRDs for obtaining this information.

<p>Food security, in Morocco as in many parts of the world, depends heavily on cereal production which fluctuates relying on weather conditions. In fact, Morocco has a production system for cereals which is dominated by rainfed. It is therefore necessary to further develop knowledge about climate change and strengthen forecasting systems for predicting the impacts of climate change.</p><p>Our research, funded by a bilateral project of Wallonie-Bruxelles International, aims to study the response of cereal production to climate change, using the dynamic vegetation model CARAIB (CARbon Assimilation In the Biosphere) developed within the Unit for Modelling of Climate and Biogeochemical Cycles (UMCCB) of the University of Liège. This spatial model includes crops and natural vegetation and may react dynamically to land use changes. Originally constructed to study vegetation dynamics and carbon cycle, it includes coupled hydrological, biogeochemical, biogeographical and fire modules. These modules respectively describe the exchange of water between the atmosphere, the soil and the vegetation, the photosynthetic production and the evolution of carbon stocks and fluxes in this vegetation-soil system. For crops, a specific module describes basic management parameters (sowing, harvest, rotation) and phenological phases.</p><p>The simulations are performed across all Morocco using different input data. The three main cereal crops simulated include soft wheat, durum wheat and barley, they are grown in all provinces and all agro-ecological zones. Regarding climatic inputs, we’re using two sets of data: the first one is interpolated and bias-corrected fields from the climate model HadGEM2-AO for the historical period (1990-2005), in addition to three different Representative Concentration Pathway scenarios (RCP2.6, RCP4.5 and RCP8.5) from 2005 to 2100. The second one is high resolution (30 arc sec) gridded climate data derived from WorldClim combined with interpolated anomalies from CRU (Climatic Research Unit) over the historical period 1990 to 2018.</p><p>After obtaining preliminary results for the past period, and in order to improve the prediction using the field data which are the observed yields, we performed a sensitivity analysis. We used the One-at-a-time (OAT) approach by moving one input variable, keeping others at their baseline (nominal) values, then, returning the variable to its nominal value, then repeating for each of the other inputs in the same way. Sensitivity may then be measured by monitoring changes in the output, using linear regression. The inputs studied are the initial value of carbon pool, leaf C/N ratio, water stress, sowing date, GDD harvest, stomatal conductance parameters, specific leaf area, and rooting depth.</p>

<p>The large-scale atmospheric circulation is one of the most important factors influencing weather and climate conditions on different timescales. Its short- and long-term changes considerably determine both mean and extreme values of surface parameters like temperature or precipitation rates. Future changes of circulation patterns are of particular interest as these may significantly alter or amplify the expected thermodynamic changes due to changing concentrations of greenhouse gases, albedo and land use. We analyse both historical as well as future climate simulations of the SMHI large ensemble (S-LENS) performed with the EC-Earth3 global climate model to examine large-scale circulation situations and their association to extremes in precipitation and temperature over Sweden. Various methods exist to classify mostly sea level pressure or geopotential height fields into characteristic circulation types, and we compare several of these methods for their applicability to represent precipitation and temperature variability over our region of interest. S-LENS consists of a 50-member ensemble for a historical period (1970-2014) and four 50-member climate change scenario ensembles covering the 21st century differing in terms of assumptions made for future radiative forcing development. We study the efficiency of circulation types in the historical period to give rise to extremes, and examine further the frequency and within-type changes of those circulation types associated with extremes by the middle and the end of the 21st century under the different climate change scenarios. S-LENS with its comparatively large number of both multi-decadal scenarios and realizations for each scenario serves as a perfect testbed to study potential changes in events of low frequency within the environment of a single model.</p>

<p>Forest fires are a global phenomenon and result from complex interactions between weather and climate conditions, ignition sources, and humans. Understanding these relationships will contribute to the development of management strategies for their mitigation and adaptation. In the context of climate change, fire hazard conditions are expected to increase in many regions of the world due to projected changes in climate, which include an increase in temperatures and the occurrence of more intense droughts. In Argentina, northwestern Patagonia is an area very sensitive to these changes because of its climate, vegetation, the urbanizations highly exposed to fires, and the proximity of two of the largest and oldest National Parks in the country. The main objective of this work is to analyze the possible influence of climate change on some atmospheric patterns related to fire danger in northwestern Argentine Patagonia. The data were obtained from two CMIP5 global climate models CSIRO-Mk3-6-0 and GFDL-ESM2G and the CMIP5 multimodel ensemble, in the historical experiment and two representative concentration pathways: RCP2.6 and RCP8.5. The data used in this study cover the region's fire season (FS), from September to April, and were divided into five periods of 20 years each, a historical period (1986-2005), which was compared with four future periods: near (2021-2040), medium (2041-2060), far (2061-2080) and very far (2081-2100). The statistical distribution of the monthly composite fields of the FS was studied for some of the main fire drivers: sea surface temperature in the region of the index EN3.4 (SST EN3.4), sea level pressure anomalies ​​(SLP), surface air temperature anomalies (TAS), the Antarctic Oscillation Index (AOI) and monthly accumulated precipitation (PR). In addition, the partial correlation coefficient was calculated to determine the independent contribution of each atmospheric variable to the Fire Weather Index (FWI), used as a proxy for the mean FS danger. As a result, we observed that SST EN3.4 is the only one that could indicate a reduction in fire danger in the future, although no variable presented a significant contribution to the FWI with respect to the others. In the RCP8.5 scenario, greater fire danger is projected by the TAS, the PR, the SLP, and relative by the AOI, while in the RCP2.6 scenario, only the TAS shows influence leading to an increase, which would be offset by the opposite influence of SST EN3.4 for the same periods in this scenario. In conclusion, in RCP8.5 it could be assumed that there is a trend towards an increase in fire danger given the influence in this sense of most of the variables analyzed, but not in RCP2.6 where there would be no significant changes.</p>

A sensor placement methodology is proposed to solve the problem of optimal location of sensors or detectors to protect population against the exposure to and effects of known and/or postulated chemical, biological, and/or radiological threats. Historical meteorological data are used to characterize weather conditions as wind speed and direction pairs with the percentage of occurrence of the pairs over the historical period. The meteorological data drive atmospheric transport and dispersion modeling of the threats, the results of which are used to calculate population at risk against standard exposure levels. Sensor locations are determined via a dynamic programming algorithm where threats captured or detected by sensors placed in prior stages are removed from consideration in subsequent stages. Moreover, the proposed methodology provides a quantification of the marginal utility of each additional sensor or detector. Thus, the criterion for halting the iterative process can be the number of detectors available, a threshold marginal utility value, or the cumulative detection of a minimum factor of the total risk value represented by all threats.

Air pollution is a major environmental threat to human health. Pollutants can reach extreme levels in the lower atmosphere when weather conditions permit. As pollutant concentrations depend on scales and processes that are not fully represented in current global circulation models (GCMs), and it is often too computationally expensive to run models with atmospheric chemistry and aerosol processes, air stagnation is often used as a proxy for pollution events with particular success in Europe. However, the variables required to identify air stagnation can have biases in GCM output, which adds uncertainty to projected trends in air stagnation. Here, the representation of air stagnation in GCMs is assessed for Europe in the historical period and in end-of-century projections based on a high-emission scenario using three methods for identifying air stagnation. The monthly frequency of stagnation during summer and autumn is projected to increase with climate change when stagnation is identified by a well-established index. However, this increase is not present when air-stagnation frequency is estimated using a statistical model based on the synoptic- to large-scale atmospheric circulation. This implies that the projected increases in air stagnation are not driven by an increase in frequency or severity of large-scale circulation events that are conducive to stagnation. Indeed, projected changes to the atmospheric circulation in GCMs, in particular a reduction in atmospheric block frequency, would suggest a reduction in future air stagnation. Additional analyses indicate that the projected increases in stagnation frequency follow the trend toward more frequent dry days, which is apparently unrelated to the large-scale drivers of air stagnation.

This study assesses the capability of ten CORDEX regional climate models (RCMs) to simulate the components of the Canadian Fire Weather Index (FWI) system across South America. Model performance for the historical period (1951-2005) is evaluated against the Copernicus Emergency Management Service ERA5 (CEMS-ERA5) reanalysis using correlation, root mean square error (RMSE), bias, and inter-model variability as validation metrics. Future projections are analyzed using the Time of Emergence (ToE) and Global Temperature of Emergence (GToE) frameworks under two representative concentration pathways (RCP4.5 and RCP8.5), to identify when and under which global warming levels fire-conducive conditions emerge beyond natural variability. Results show that FWI and the Fine Fuel Moisture Code (FFMC) reproduce observed variability most accurately (r > 0.70), while cumulative indices such as the Drought Code (DC) and Duff Moisture Code (DMC) exhibit larger uncertainties linked to precipitation biases and long-term moisture processes. Projections indicate that extreme fire weather conditions (represented by the frequency of days exceeding the 95th percentile of FWI, FWI95d) emerge earliest and most extensively, particularly under the high-emission RCP8.5 scenario, with large areas of northern and central South America crossing emergence thresholds during the early years of the 2030s decade. In contrast, indices related to fire-season length (FWIfwsl) and seasonal severity (FWIfs) show delayed and spatially fragmented emergence patterns, reflecting their stronger dependence on persistent warming trends.
A critical finding is that several regions, including southern Brazil, Paraguay, northern Argentina, and Bolivia, have already exceeded emergence thresholds during the historical period, signaling that shifts in fire-weather regimes are already underway. These results highlight the urgent need to strengthen fire management and adaptation strategies across South America, while emphasizing that limiting global warming below 2 °C would significantly delay and reduce the extent of emerging fire danger in the region.

There are specific construction operations that require weather forecast data to make short-term decisions regarding construction; however, most resource-related decision making and all project management plans must be carried out to anticipate weather conditions beyond the capabilities of the currently available forecasting technologies. In this study, a series of single- and multi-risk analyses were performed with ~9 km grid resolution over Türkiye using combinations of weather and climate variables and their threshold values which have an impact on the execution and performance of construction activities. These analyses will improve the predictability of potential delays, enable the project to be scheduled on a future-proof basis by considering the calculated normal and periodic predictions on the grid scale, and serve as a dispute resolution tool for related claims. A comprehensive case study showcasing the methodology and illustrating its application shows that the project duration is expected to be extended because of the impact of climate on both historical and future periods. While the original project duration was 207 days, when climate effects were considered, the optimum mean and median values increased to 255 and 238 days, respectively, for the historical period. The optimum duration mean and median change to 239 days by the end of the century, according to the SSP5-8.5 scenario, if the construction schedules consider climate change. The change in duration was mainly due to rising temperatures, which increased winter workability and reduced summer workability. However, if the historical practices are carried over to future schedules, the mean and median increase to 258 days and 244 days, respectively, which may cause unavoidable direct, indirect, or overhead costs.

Grape berries and their yields can be strongly affected by the complex connections and interactions between the grapevines and the conditions of the local environment. In areas of known wine production, the yield and quality are usually improved by considering the climate, planting the best grape variety, and using specific agricultural techniques. Thus, sustainability in the winemaking sector worldwide is under pressure due to ongoing climate change, requiring adaptation at multiple levels. Portuguese vineyards will experience increasingly dry and warm conditions due to climate change, with varying degrees of intensity and frequency of weather extremes. Nevertheless, the potential effects of these extraordinary occasions and their effects on viticulture in the future are not well known. In this research, we calculated seventeen climate extreme indices for the Portuguese wine denomination of origin regions/subregions in the historical period (1981–2010) and future periods (2041–2070 and 2071–2100), under the Representative Concentration Pathway 8.5, and based on a five-member ensemble of Regional Climate Model-Global Climate Model chain simulations. Moreover, a principal component analysis was performed for both precipitation and temperature extremes independent of each other. All of Portugal's wine regions experienced an increase in temperature extremes, predominantly in the westernmost regions. When it comes to the precipitation extremes, they show a decrease in the future and a general decline in precipitation but still are a major risk in the northeastern regions. In contrast, the dry extremes, likely bringing on severe droughts, will become much stronger. Finally, it was then possible to recognize which wine regions will be the most vulnerable to extreme weather conditions in the future. This information is essential for enabling smarter choices in the sector, including for long-term planning, climate change adaptation and risk reduction. Acknowledgments: Soil recover for a healthy food and quality of life (SoilRec4+Health). Projeto cofinanciado pelo Fundo Europeu de Desenvolvimento Regional (FEDER) através do Programa Operacional Regional do Norte (NORTE-01-0145-FEDER-000083).

In Japan, diaries that record daily weather conditions since the 18th century have been kept in various parts of the country. The winter weather distribution in the Japanese archipelago is characterized by a north-south extension of the mountain ranges, with the Sea of Japan side having more snow days and the Pacific side having more sunny days. On the other hand, when temperatures are higher in winter, precipitation is more likely to be rain, and when temperatures are lower, it is more likely to be snow. Therefore, by obtaining a linear regression equation based on the correlation between the ratio of the number of snowfall days to the total number of precipitation days in winter and the average temperature for the period when meteorological observation records are available (since 1879), it is possible to reconstruct long-term temperature variations from the snowfall ratio obtained from diary weather records in historical periods. Based on this principle, we attempted to reconstruct winter temperatures in Nagasaki since 1700. Fortunately, Nagasaki has records of early meteorological observations by Dutch medical doctors from 1840 to 1860, before the start of official meteorological observations, which can be used to verify the reconstructed temperatures. The results show that winter temperatures in Nagasaki tended to be lower in the period from about 1700 to 1840, but warmed temporarily in the 1850s and 1860s, and then remained lower again until about 1950. The temperature reconstructions for 1840-1860, when the reconstructed and observed temperatures overlap, are almost coincident, demonstrating that the winter temperature reconstructions based on snowfall ratios are mostly accurate.

European windstorms are among most important natural hazards for insurance companies. Quantifying with the impact of windstorms becomes more challenging due to seasonal loss clustering, characterized by numerous intense windstorms in a season, leading to exceptionally high seasonal losses. Climate change introduces another level of uncertainty regarding potential losses from European windstorm events.The EURO-CORDEX dataset is designed to enhance the representation of regional and local weather conditions consists of a set of high-resolution climate simulations at 12.5 km resolution.  In this context, this will allow the assessment of the impact of windstorms for recent and future climates in a finer resolution. To achieve this, we use daily maximum surface wind gusts of 20 global-to-regional climate model chains from EURO-CORDEX (EUR-11 domain). The investigation focuses on the extended winter season (ONDJFM) between the historical period (1976-2005) and future projections under global warming level (GWL) scenarios of +2°C and +3°C, following the Representative Concentration Pathway 8.5 (2006-2100).The evaluation of windstorm impact is carried out using the Loss Index (LI) method, focusing on the country level. For the historical period, a substantial bias is observed in the 98th percentile of daily maximum wind gusts between EURO-CORDEX and ERA5. This bias is corrected through empirical quantile mapping, resulting in corrected models that show reduced biases in wind gust extremes while maintaining consistency with the climate change signal.Under the +2°C and +3°C GWLs, the majority of models indicates a reduction in the magnitude and frequency of extreme windstorms over Western Europe and the Iberian Peninsula, leading to decreased European windstorm loss, while an increase over Eastern Europe is expected, contributing to higher loss. In the majority of countries, the occurrence of seasonal loss clustering is expected to decrease under GWL conditions compared to the current climate.Our study provides valuable insights for insurance companies and policymakers to deal with the uncertainty of the loss of windstorm under future climate conditions.

Future fire weather conditions under climate change were investigated based on the Fire Weather Index (FWI), Initial Spread Index (ISI) and threshold-specific indicators in Greece. The indices were calculated from climate datasets derived from high-resolution validated simulations of 5 km. The dynamical downscaled simulations with the WRF model were driven by EC-Earth output for historical (1980–2004) and future periods, under two Representative Concentration Pathways (RCPs), RCP4.5 and 8.5. The analysis showed that the FWI is expected to increase substantially, particularly in the southern parts with extreme values found above 100. In addition, the number of days with an FWI above the 90th percentile is projected to increase considerably (above 30 days), under both scenarios. Over the eastern and northern mainland, the increase is estimated with more than 70 days under RCP4.5, in the near future (2025–2049). Moreover, central and north-eastern parts of the country will be affected with 30 or more extreme consecutive days of prolonged fire weather, under RCP4.5, in the near future and under RCP8.5 in the far future (2075–2099). Finally, the expected rate of fire spread is more spatially extended all over the country and particularly from southern to northern parts compared to the historical state.

In this study we analysed the climatic conditions for infiltration estimation, different calculation methods and infiltration impact on heat load for heating systems dimensioning. To determine the wind conditions at low air temperatures of the coastal- and inland climatic zones in Estonia, 42 years of climatic data for Tallinn and Tartu were investigated. Calculation models with detailed air leakages were constructed of a single and two-storey detached house using dynamic simulation software IDA ICE. Simulations were carried out with the constructed calculation models, simulating various wind and sheltering conditions to determine the heating load of the buildings under measured wind conditions at the design external air temperatures. The simulation results were compared with results calculated with European Standard EN 12831:2017, methodology given in the Estonian regulation for calculating energy performance of buildings and with simulations using the default settings in IDA ICE based on the ASHRAE design day conditions. The percentage of heat losses caused by infiltration was found as 13-16% of all heat losses for the studied buildings. Simulations with historical climate periods showed that even in windy weather conditions the heating system dimensioned by the methods analysed may not be able to provide the required indoor air temperature. Analysis using the coldest and windiest periods showed that when systems are dimensioned by the studied methods, the highest decline in indoor air temperature occurs on the windiest day and not on the coldest day. The impact of high wind speeds and low sheltering conditions resulted up to 50% of all heat losses.

Development and accuracy assessment of a 12-digit hydrologic unit code based real-time climate database for hydrologic models in the US