Climate Classification Research Articles

A meta-analytic review on the spatial and climatic distribution of meteorological drought indices

Abnormalities in climate in recent years have been attributed to climate change. In particular, an increase in the frequency of droughts has started to influence the way we live. There is an urgent need to study and monitor droughts to prevent severe damage. Drought monitoring research is conducted using the meteorological drought index, a compilation of weather data. Although weather data are highly dependent on climatic zones, existing studies have not considered the drought index by climatic zones. This study aims to identify the spatial and climatic distribution of meteorological drought index studies and provide a well-matched combination of the drought index and climatic classification through a meta-analytic review. Data were used in the form of research papers covering the Palmer Drought Severity Index (PDSI), Standardized Precipitation Index (SPI), and Standardized Precipitation Evapotranspiration Index (SPEI) published from 2011 to 2021. The trends showed that SPEI has become a leading index for the study of drought monitoring among the three drought indices since 2015. Additionally, Asia was the focus of much of this drought research due to climate events in China. Each continent and climate classification was assigned to a different dominant drought index. For example, PDSI was commonly used in North America and continental climates, SPI in Africa, Europe, South America, and tropical and dry climates, and SPEI in Asia and temperate climates. This evaluates countries, in which drought monitoring studies are rare, using a meteorological index based on the same climatic zones. Monitoring and predicting drought patterns would improve the basic needs of humankind.

Forecasting future climate boundary maps (2021–2060) using exponential smoothing method and GIS

Future-oriented forecasts have an important place in making forward-looking decisions and planning. At the beginning of these studies is the monitoring and detection of climate change. The climate is very variable. Therefore, by making predictions about the climate, preliminary information about how and to what extent the climate will change can be obtained, and accordingly, necessary precautions can be taken quickly. This study aims to produce predictive climate boundary maps using Geographic Information Systems (GIS), in which climate classification methods and time series methods are evaluated to monitor and determine the changes caused by the climate in 13 selected provinces in Turkey. The triple exponential smoothing method and the Erinc climate classification method were discussed. The data were obtained from the General Directorate of Meteorology (GDM) between 1930 and 2020, and each year's precipitation efficiency index (Im) of the Erinc climate classification method was calculated. It is divided into two classes for forecasting and testing current indices: test Im indices (1930–2014) and forecast test Im indices (2015–2020). MAD, MSE, and MAPE criteria were calculated to determine whether the Im estimates were meaningful. However, the accuracy of the estimates was ensured by considering the MAPE criteria for this study. After this stage, the analyses were performed again with test Im indices (1930–2020) and forecast Im indices (2021–2060), and Im indices predictions for the future were made. Finally, the obtained forecast indices were subjected to GIS interpolation analyses (Kriging and IDW), and future climate boundary maps were produced. Thanks to the outputs obtained from the study, how the climate classes of any region will be in the future and to what extent they will change will be provided by evaluating the climate classification and time series methods together. It will contribute to different studies in this field with its innovative analysis approach.

Influence of climate stress on technical efficiency and economic downside risk exposure of EU dairy farms

AbstractThis paper assesses the influence of heat and drought stress on the economic performance of the European dairy sector. Climatic data from the Gridded Agro-Meteorological data in Europe were combined with dairy enterprise data from the Farm Accountancy Data Network, resulting in a data set of 4412 farms in 22 European Union (EU) countries over the period 2007–2013. Since the performance of dairy farms is influenced by the context in which they operate, farms were grouped into areas representing similar climatic conditions through the use of a latent class analysis. Technical efficiency (TE) and economic downside risk were used as performance indicators against which the effect of climatic stress factors was evaluated. TE was estimated using a ‘true-fixed’ effect stochastic frontier model. Economic downside risk was based on gross margin deviations. Regression analysis suggests a significant negative effect of drought and heat stress on both TE and the downside gross margin difference in most climatic classes, with few exceptions. Results imply that both drought and heat stress-related issues need to be considered when designing adaptation strategies to address threats to the economic performance of the EU dairy sector.

Accuracy Evaluation and Analysis of GNSS Tropospheric Delay Inversion from Meteorological Reanalysis Data

Accurate estimation of tropospheric delay is significant for global navigation satellite system’s (GNSS) high-precision navigation and positioning. However, due to the random and contingent changes in weather conditions and water vapor factors, the classical tropospheric delay model cannot accurately reflect changes in tropospheric delay. In recent years, with the development of meteorological observation/detection and numerical weather prediction (NWP) technology, the accuracy and resolution of meteorological reanalysis data have been effectively improved, providing a new solution for the inversion and modeling of regional or global tropospheric delays. Here, we evaluate the consistency and accuracy of three different types of reanalysis data (i.e., ERA5, MERRA2, and CRA40) used to invert the zenith tropospheric delay (ZTD) from 436 international GNSS service (IGS) stations in 2020, based on the integral method. The results show that the ZTD inversion of the three types of reanalysis data was consistent with the IGS ZTD, even in heavy rain conditions. Furthermore, the average precision of the ZTD inversion of the ERA5 reanalysis data was higher, where the mean deviation (bias), mean absolute error (MAE), and root mean square (RMS) were –3.39, 9.69, and 12.55 mm, respectively. The ZTD average precisions of the MERRA2 and CRA40 inversions were comparable, showing slightly worse performance than the ERA5. In addition, we further analyzed the global distribution characteristics of the ZTD errors inverted from the reanalysis data. The results show that ZTD errors inverted from the reanalysis data were highly correlated with station latitude and climate type, and they were mainly concentrated in the tropical climate zone at low latitudes. Compared to dividing error areas by latitude, dividing error areas by climatic category could better reflect the global distribution of errors and would also provide a data reference for the establishment of tropospheric delay models considering climate type.

Updated Trewartha climate classification with four climate change scenarios

AbstractThe Updated Trewartha climate classification (TWCC) at global level shows the changes that are expected as a consequence of global temperature increase and imbalance of precipitation. This type of classification is more precise than the Köppen climate classification. Predictions included the increase in global temperature (T in °C) and change in the amount of precipitation (PA in mm). Two climate models MIROC6 and IPSL‐CM6A‐ LR were used, along with 4261 meteorological stations from which the data on temperature and precipitation were taken. These climate models were used because they represent the most extreme models in the CMIP6 database. Four scenarios of climate change and their territories were analysed in accordance with the TWCC classification. Four scenarios of representative concentration pathway (RCP) by 2.6, 4.5, 6.0 and 8.5 W/m2 follow the increase of temperature between 0.3°C and 4.3°C in relation to precipitation and are being analysed for the periods 2021–2040, 2041–2060, 2061–2080 and 2081–2100. The biggest extremes are shown in the last grid for the period 2081–2100, reflecting the increase of T up to 4.3°C. With the help of GIS (geographical information systems) and spatial analyses, it is possible to estimate the changes in climate zones as well as their movement. Australia and South East Asia will suffer the biggest changes of biomes, followed by South America and North America. Climate belts to undergo the biggest change due to such temperature according to TWCC are Ar, Am, Aw and BS, BW, E, Ft and Fi. The Antarctic will lose 11.5% of the territory under Fi and Ft climates within the period between 2081 and 2100. The conclusion is that the climates BW, Bwh and Bwk, which represent the deserts, will increase by 119.8% with the increase of T by 4.3°C.

Agroclimatic zoning of the state of Nayarit, México

Agriculture productivity in the state of Nayarit has decreased since 1998. The aim of the study was to undertake the agroclimatic zoning across the state in order to determine the type of crops more convenient to render the highest yields, based on Papadakis climate classification system. Hydric and thermal characteristics pertaining to the geographic distribution of crops were used, as well as indexes derived from meteorological data provided by 25 climate stations. There were three climatic groups identified: tropical, subtropical and cold land, having four, three and two subgroups each, respectively. First two climatic groups support winter cereals such as oat (Avena sativa L.), barley (Hordeum vulgare L.), rye (Secale cereale L.) and wheat (Triticum aestivum L.); and summer cereals such as corn (Zea mays L.), millet (Panicum italicum L.), rice (Oryza sativa L.) and sorghum (Sorghum bicolor (L.) Moench); in addition to banana (Musa paradisiaca L.), citrus and potato (Solanum tuberosum L.) and sugar cane (Saccharum officinarum L.). On the other hand, corn and potato were found in the cold land climatic group. Based on Papadakis’ methodology, for each climatic sub-group identified, a set of recommendation management were given to improve yields: crop type, sowing season, irrigation, fertilizing and other agrochemicals application; and to avoid crop damage. Agroclimatic zoning map was generated by using the inverse distance weighted interpolation method. This study may contribute to the successful planning of crops across the region and thus improving the state’s economy.

Evolution of the Atlantic Intertropical Convergence Zone, and the South American and African Monsoons Over the Past 95‐Myr and Their Impact on the Tropical Rainforests

AbstractThe widening of the South Atlantic Basin led to the reorganization of regional atmospheric and oceanic circulations. However, the response of the Atlantic Intertropical Convergence Zone (ITCZ), and South American and African monsoons across paleoclimate states, especially under constant paleogeographic and climatic changes, has not been well understood. Here we report on paleoclimate simulations of the Cenomanian (∼95 Ma), early Eocene (∼55 Ma), and middle Miocene (∼14 Ma) using the Community Earth System Model version 1.2 to understand how the migration of the South American and African continents to their modern‐day positions, uplift of the Andes and East African Rift Zone, and the decline of atmospheric CO2 changed the Atlantic ITCZ, and the South American and African monsoons and rainforests. Our work demonstrates that the South Atlantic widening developed the Atlantic ITCZ. The South Atlantic widening and Andean orogeny led to a stronger South American monsoon. We find the orogeny of the East African Rift Zone is the primary mechanism that strengthened the East African monsoon, whereas the West African monsoon became weaker through time as West Africa migrated toward the subtropics and CO2 levels fell below 500 ppm. We utilize the Köppen‐Geiger Climate Classification as an indicator for maximum rainforest extent. We find that during the Cenomanian and early Eocene, a Pan‐African rainforest existed, while the Amazon rainforest was restricted toward the northwestern corner of South America. During the middle Miocene, the Pan‐African rainforest was reduced to near its modern‐day size, while the Amazon rainforest expanded eastward.

Energy and comfort evaluation of a novel hybrid control algorithm for smart electrochromic windows: A simulation study

It is estimated that about 79% of the energy consumption in buildings is related to lighting, cooling, and heating. On the other hand, studies showed that the use of smart electrochromic windows by controlling the solar radiation entering the building can affect the energy consumption in buildings. Each smart electrochromic window consists of two basic parts, the electrochromic coating and the control algorithm, which can be modified to improve its energy efficiency. In this study, a hybrid control algorithm is introduced to manage the transparency status of the smart electrochromic window, which uses different algorithms according to the user's presence in the building. When the user is in the building, a just-in-time control algorithm is used, and when the user is not in the building, the value of the decision variable is predicted for future time steps using a deep learning model. Considering the 15-minute time-step, the energy consumption of a building in the mentioned sections was simulated for 6 different climate classes in Iran in 2019. In this study, Fanger’s PMV thermal comfort index was used as the decision variable and the results showed that when this index is in [-0.5,+0.5], the electrochromic window should be transparent. The results showed that the deep learning model must have 5000 nodes and 10 hidden layers to achieve a stable accuracy and the best results were only due to the forecast of the next 15 min. Finally, the results showed that the proposed hybrid control algorithm has been able to increase energy-saving in the mentioned sections of the studied building by at least 26.12% (averagely 31.02%) in all studied climate classes. Also, performing a Pearson Correlation Analysis showed that the hybrid control algorithm was sensitive to the annual indices of maximum temperature, mean temperature, relative humidity, day length, daily sunshine, and global horizontal irradiation. The results showed that the proposed algorithm increased energy efficiency by at least 8.44% compared to its constituent algorithms.

The Cooling Station: Combining hydronic radiant cooling and daytime radiative cooling for urban shelters

Global warming has tried the world with challenges like increased cooling energy demand, heatwaves, and unbearable summers, with accentuated nuances in dense urban areas. Here heat island effects are most pronounced and heat-safe pockets are a necessity rather than a comfort. Bus stops can act as shelters for commuters with the advantage of usually being well distributed across the cities in the form of semi-enclosed spaces. Daytime radiative cooling is a novel technology that enables free cooling of a substance under direct solar radiation. In this study, radiative cooling is combined with the hydronic radiant cooling technology in the integrated design of the Cooling Station, a bus shelter capable of providing energy-free urban thermal comfort throughout the summer. The study aims at evaluating the effect of geometry, orientation, surrounding elements, and climate on the performance of the Cooling Station. It is found that humidity and surrounding buildings diminish the performance of the radiative cooling panels, but the penalty can be significantly mitigated by applying non-reciprocal asymmetric transmission windows on top of the panels. The results indicate that the optimized design of the Cooling Station is capable of decreasing the Universal Thermal Climate Index (UTCI) by up to 10 °C in the considered scenario of a mid-rise area in Tehran, the capital of Iran. Further, the performance evaluation across all Köppen-Geiger climate classes demonstrates that in hot and semi-arid climates, the Cooling Station develops its full potential.

Detailed investigation of discrepancies in Köppen-Geiger climate classification using seven global gridded products

The Köppen-Geiger (KG) climate classification has been widely used to determine the climate at global and regional scales using precipitation and temperature data. KG maps are typically developed using a single product; however, uncertainties in KG climate types resulting from different precipitation and temperature datasets have not been explored in detail. Here, we assess seven global datasets to show uncertainties in KG classification from 1980 to 2017. Using a pairwise comparison at global and zonal scales, we quantify the similarity among the seven KG maps. Gauge- and reanalysis-based KG maps have a notable difference. Spatially, the highest and lowest similarity is observed for the North and South Temperate zones, respectively. Notably, 17% of grids among the seven maps show variations even in the major KG climate types, while 35% of grids are described by more than one KG climate subtype. Strong uncertainty is observed in south Asia, central and south Africa, western America, and northeastern Australia. We created two KG master maps (0.5° resolution) by merging the climate maps directly and by combining the precipitation and temperature data from the seven datasets. These master maps are more robust than the individual ones showing coherent spatial patterns. This study reveals the large uncertainty in climate classification and offers two robust KG maps that may help to better evaluate historical climate and quantify future climate shifts.

Cultural skills for the municipality of Recife - Pernambuco, Brazil

The objective is to characterize the climate and to carry out zoning and agroclimatic aptitude for pineapple, herbaceous cotton, banana, sugarcane, beans, corn, cassava, castor bean, sisal and sorghum crops, pointing out their possible cultivation skills in the city Recife - PE. A historical series of rainfall and mean air temperature was used to calculate climatological water balance, climatic classification, evapopluviogram construction and agroclimatic zoning of the crops. Recife - PE has full aptitude for the cultivations of pineapple, herbaceous cotton, beans, castor bean, cassava and sisal. For the cultivation of cashew, maize, sugarcane and sorghum we contacted moderate aptitude. Only banana cultivation is restricted due to the region having a marked water deficit. The elements temperature, rainfall were determinant for the calculation of the water balance and the definition of the pluviometric scenarios. The study provides support to decision makers by providing information on Climatological Water Balance, climatic classification, agroclimatic zoning and crop aptitude, favoring adequate planning of agricultural activities and consequently reduction of crop risks.

Southeastern Brazil inland tropicalization: Köppen system applied for detecting climate change throughout 100years of meteorological observed data.

Many regions around the world are facing climate changes, with substantial increase in air temperature over the past decades, which is mainly related to continental and global warming forced by the higher greenhouse gas (GHG) emissions. The objectives of this study were to use the Köppen climate classification to detect local climate change based on a historical series of 100 years and to assess if such change is related to those that are occurring in other spatial scales as a likely consequence of increasing GHG. This paper brings a content full of innovative results. The study area presented an average annual air temperature increase by 0.9 °C between 1917 and 2016, rising from 21.4 °C for the first climatological normal (1917–1946) to 22.3 °C for the last one (1987–2016). Furthermore, in the summer months, the temperature rose from 24.5 to 25.3 °C, and in the winter months, such increase was from 17.1 (1917–1946) to 18.3 °C (1987–2016). Our findings showed the subtropical conditions (Cfa in Köppen’s classification) in the study area persisted from the beginning of the analysis (1917–1946) until the climatological normal of 1979–2008, with a clear tendency of tropicalization after that with a change in the climate type of Piracicaba from subtropical to tropical, which can now be classified as tropical with dry winter (Aw climate type). The local average air temperature showed concordances with the long-term air temperature anomalies from regional, continental, and global scales, indicating that all of them may be linked with increasing GHG emissions, since well-defined long-term linear relationships (r2 = 0.99) were observed between continental and global average air temperature anomalies and atmospheric CO2 concentration observed at the NOAA Lab in Mauna Loa in the last 59 years. While the local and regional forcing effects remain to be fully unraveled, our study provided a valid and strong scientific sound evidence that climate change occurred in Piracicaba, southeastern Brazil, in the last 100 years.

Evapotranspiration and Vegetation Cover Classifications Maps Based on Cloud Computing at the Arab Countries Scale

According to the most recent Köppen–Geiger classification, Arab countries are divided into seven climate classes. Ground data availability is limited in developing countries, and ground meteorological data are scarce and concentrated in a few locations, rather than station maintenance capability being adequate for the responsibilities. The current study uses remote sensing and meteorological data to create regional classification maps of reference evapotranspiration (ETo), potential crop evapotranspiration, and vegetation cover in Arab countries from 2005 to 2020. The Stand-alone Remote Sensing Approach to Estimate Reference Evapotranspiration (SARE) was used to estimate ETo using satellite data from 2005 to 2020. The Land Surface Temperature (LST) and Normalized Difference Vegetation Index (NDVI) were extracted from MODIS satellite data and used in the SARE model, in addition to elevation (E), Julian day (J), and Latitude (Lat). To validate the SARE model results, the FAO-Penman–Monteith model was applied to 35 ground meteorological stations distributed across Arab countries to cover all climate classes based on the most recent Köppen–Geiger climate classification. Google Earth Engine was used to create the classification. The statistical indices produced acceptable results, with average RMSE values ranging from 6.9 to 17.3 (mm/month), while correlation coefficient (r) and index of agreement (d) values are more significant than 0.9. To be included in the ETc calculation, the crop coefficient (Kc) was calculated using NDVI 250 m spatial resolution. The density of the vegetation cover is used to classify it (low to high). The average vegetation cover was calculated to be greater than 31.5 Mha. The minimum vegetation cover was 14.9 Mha, and the maximum vegetation cover was 49.2 Mha. 15.8 Mha can be cultivated without supplementary irrigation for at least one agricultural season, according to the rainfall classification map.

Scientific evidence of sustainable plant disease protection strategies for oilseed rape (Brassica napus) in Sweden: a systematic map

BackgroundOilseed rape (OSR; Brassica napus L.) is a highly valued crop for food, feed and industrial use. It is primarily grown in temperate climates, and over recent decades, its area of production and profitability have increased. Concurrently, several diseases negatively impact OSR production. Diseases caused by soil-borne pathogens, pose a risk of substantial yield loss since crop rotation schemes have become narrow as the time lapse between OSR crops in a field has been shortened. The aims of this paper were to provide an overview of plant protection measures available for OSR production and to identify knowledge gaps and areas where more research is needed.MethodsThis systematic map builds on a previously published protocol and follows the ROSES reporting standard. The search strategy was developed in collaboration with stakeholders and designed to cover available scientific evidence for OSR disease management in climate zones relevant for Scandinavian crop production (Dfc, Dfb, Cfb and Cfa in the Köppen-Geiger climate classification). Five scientific databases were used to identify peer-reviewed literature, complemented by additional searches performed in grey literature. Articles were screened at three stages: the title, abstract and full text. The eligible publications included studies of OSR crops, and all measures to control crop disease in agricultural fields were considered eligible interventions. The comparator was intervention and no intervention, and the yield per unit area, disease suppression or an increase in crop quality were determined to be outcomes of interventions. A basic assessment of the experimental design of each study was performed to assess its eligibility. All articles were coded based on the following categories: the location and climate zone, disease, pathogen, intervention and management method, outcome and study design. Articles not reporting original data but judged to be relevant (i.e., review papers, books and notes of registration of cultivars) were saved in a separate category called “books, reviews and reports”.Review findingsA total of 4633 articles were collected through systematic searches. After duplicates were removed, 3513 articles were included in the screening process. After screening at the title and abstract levels, 897 articles were evaluated at the full text level, and 118 articles comprised the studies that met the eligibility criteria of the systematic map. The country (Canada) and region (Europe) with the largest OSR crop production areas also contributed the highest number of articles. In total, 17 different diseases were reported, with black leg (syn. Phoma stem canker) being the most studied disease. Nineteen different intervention methods or management types were examined. Cultivar resistance and pesticide application were the most studied control measures.ConclusionWe report scientific studies on plant disease protection measures for OSR based on field trials where the results are intended to be directly implemented in crop production management. The map clearly provides an overview of research progress throughout the time period chosen, and it identifies knowledge gaps regarding important diseases where only a few studies have been published, for example, diseases caused by viruses.

A climatic classification of the world’s wine regions

Using a dataset with 16 climate variables for locations representing 813 wine regions that cover 99 % of the world’s winegrape area, we employ principal component analysis (PCA) for data reduction and cluster analysis for grouping similar regions. The PCA resulted in three components explaining 89 % of the variation in the data, with loadings that differentiate between locations that are warm/dry from cool/wet, low from high diurnal temperature ranges, low from high nighttime temperatures during ripening, and low from high vapour pressure deficits. The cluster analysis, based on these three principal components, resulted in three clusters defining wine regions globally, with the results showing that premium wine regions can be found across each of the climate types. This is, to our knowledge, the first such classification of virtually all of the world’s wine regions. However, with both climate change and an increasing preference for premium relative to non-premium wines, many of the world’s winegrowers may need to change their mixes of varieties, or source more of their grapes from more appropriate climates.

Northward Expansion of Desert Climate in Central Asia in Recent Decades

AbstractClimate variation in Central Asia is examined using changes of climate classification to obtain geographically specific information of the variation across the region. Major results show the northward expansion of desert climate by over 100 km in mid‐latitudes of the region since the 1980s. In the meantime, all types of climates in the region have increased temperatures. In mountainous areas, previous cold climate is replaced by warmer and also wetter climate. These changes could have largely resulted in the fast retreat of glaciers and the temporary rise of groundwater and the water levels of lakes in the drainage areas in recent decades. Because different climate types are associated with specific flora and fauna, the shifts of the climate types in locations have elevated the potential to initiate new feedbacks of the ecological systems to the climate and complicate its variation.

Fine-Grained Climate Classification for the Qaidam Basin

The Qaidam Basin is a sensitive climate transition zone revealing a wide spectrum of local climates and their variability. In order to obtain an objective and quantitative expression of local climate regions as well as avoid the challenge to pre-define the number of heterogeneous local climates, the ISODATA cluster method is employed to achieve the fine-grained climate divisions of the Qaidam Basin, which can heuristically alter the number of clusters based on the input of monthly temperature and precipitation data. The fine-grained climate classification extends the traditional Köppen climate classification and represents the complex climate transformation processes in terms of fine-grained climate clusters. The following results are observed: (i) The Qaidam Basin is divided into an arid desert basin area and the surrounding alpine mountainous areas. The climate distribution is affected by both the altitude and the dryness ratio, which, employing the Budyko framework, largely characterizes the local energy–water fluxes at the surface and the related vegetation regimes (biomes). The fine-grained climate classification successfully captures their causal relationships and represents them well by the local climates: the climatic spatial differentiation in the mountainous areas is highly consistent with the topography and reveals an elevation-dependent circular distribution from the edges to the center of the basin; the climate heterogeneity within the basin presents a west-to-east meridional distribution due to the combined effect of the mid-latitude westerlies and the Indian monsoon. (ii) The climate gradients are spatially different over the Qaidam Basin. The surrounding mountainous areas have a large climate gradient compared to the inner basin; the southern mountain edge is governed by a more severe climate change than the north-eastern one; and the climate gradient is larger in the eastern than in the western basin. (iii) The lake regions within the basin show an obvious lake effect and reveal a local lake climate. Spatially, a common structure emerges with a dryer-climate zone or watershed embedding a wetter lake-affected area, which appears to migrate eastward becoming stepwise wetter from the very dry center to the wet eastern boundary of the Qaidam basin. This provides a topographically induced insight of the wet climate expansion of initially arid climates and is crucial to improve the Qaidam Basin’s ecological environment. Finally, although this work mainly focuses on the local-scale climates and their variability in the Qaidam Basin, the data-driven cluster methodology for climate refinement is transferable to regional- even global-scale climate studies, which offers broad application prospects.

Global soil organic carbon–climate interactions: Why scales matter

AbstractSoil organic carbon (SOC) holds the largest terrestrial carbon stock because of soil conditions and processes that favor soil carbon persistence. Vulnerable to climate change, SOC may cross a tipping point toward liberating carbon‐based greenhouse gases, implying massive self‐amplifying SOC‐ climate interactions. Estimates of SOC persistence are challenging as we still lack broad mechanistic insights. Upscaling mechanistic details from small to larger scales is challenging because the driving factors are not available at the needed resolution. Downscaling is problematic as many modeling studies point to the highest uncertainties deriving from the SOC response to climate change, while models themselves have difficulties in replicating contemporary soil properties and dynamics. To bridge the problems of scaling, strict process orientation seems adequate. Holdridge Life Zones (HLZ) classification, as one example, is a climate classification framework at a mesoscale that provides a descriptive approach to facilitate the identification of potential hotspots and coldspots of SOC‐climate interaction. Establishing coordinated experiments across all HLZ, but also including multiple global change drivers, has the potential to advance our understanding of general principles regulating SOC‐climate interaction and SOC persistence. Therefore, regionally tailored solutions for both experiments and modeling are urgently needed and can lead to better management of soil and the ecosystem services provided. Improving “translations” from the scales relevant for process understanding to the scales of decision‐making is key to good management and to predict the fate of our largest terrestrial carbon stock.This article is categorized under: Integrated Assessment of Climate Change > Integrated Scenario Development

Multifactorial Principal‐Monotonicity Inference for Macro‐Scale Distributed Hydrologic Modeling

AbstractMacro‐scale distributed hydrologic modeling advanced hydro‐system understandings and scientized relevant human activities. However, it faces challenges of hydrometeorological heterogeneities, parametric interactions, data uncertainty, computational expensiveness, and other complexities, especially over cold regions with intense climatic changes. As an effort to address them, a multifactorial principal‐monotonicity inference (MFPMI) method is developed through integrating, extending, or improving climate classification, hydrologic modeling, and sensitivity analysis. MFPMI is applied to an undammed macro‐scale high‐latitude cold‐region watershed, Athabasca River Basin (ARB) in Canada. MFPMI mitigates the underestimation of climatic impacts on streamflows in process‐based models, hydrologic classification, large‐scale hydroclimatic data deconstruction, parametric‐interaction neglection, and climatic homogenization; its superiority is particularly evident for highly heterogeneous climates. Dominant climatic impacts on ARB streamflows of various regimes increase from tributaries to the mainstem and decrease from up‐ through down‐ to mid‐stream catchments, possibly due to the offset effects of non‐climatic factors (e.g., vegetation and soil). The impacts also decline with streamflow magnitudes, and vary with seasons, spatial scales and lead months rather than temporal resolutions. Streamflow magnitudes, catchments and metrics differ compositions of the climate conditions explaining cross‐scale uppermost discharge variations. In spite of this, streamflow increases with temperature and precipitation, and headwater climates play part of dominant roles in forcing discharges throughout ARB. Accuracy metrics differentiate parameters and accordingly structures of MFPMI models, and hydroclimatic data uncertainty is high for high flows, fine temporal scales or low climatic impacts, increasing uncertainty in hydrologic simulations and climatic‐impact estimates. This study helps advance modeling and understandings of macro‐scale cold‐region hydrology.

Mapping Climate Zones of Iran Using Hybrid Interpolation Methods

Climate plays a key role in ecosystem services. Understanding microclimate change can be a significant help in making the right decision for ecosystems and buffering the effects of global warming. Given the large distances between meteorological stations and the changes in the climate variables within short distances, such variations cannot be detected just by using observed meteorological data. This study aimed at determining the spatial structure of the mean annual temperature, the annual average precipitation, and the climate zoning of Iran using data from 3825 stations from 2002 to 2016.The multivariate regression demonstrated the dependence of these variables on longitude, latitude, and elevation. Regression-kriging indicated a decline in temperature from east to west and northwest in high-altitude areas, while most precipitation values were observed over the Caspian Sea coastline and the Zagros Mountains. Climatic zoning showed that using auxiliary variables was very effective in detecting 24 climatic classes and understating the climate diversity in Iran. Hot to very hot and arid to very arid climate classes occupy the largest part of Iran, including the southeastern and southern desert regions. According to the generated climatic map, the large climatic diversity of Iran needs accurate policymaking regarding cultivation patterns and biodiversity. Visual comparisons of climatic zones with four remotely sensed agricultural-related variables showed that using such carefully produced climatic maps would be beneficial in classifying, assessing, and interpreting the remote sensed agricultural-related variables.