How do spatial scale-dependent urban and forest areas influence regionally seasonal temperatures in mid-latitude South Korean cities?

This study aims to investigate the effects of urban and forest areas measured in three dimensions on seasonal temperature over forty years in South Korean cities. We measure the urban and forest areas at the city, neighborhood, and spatially clustered levels in four periods every ten years. Using Hot Spot Analysis (Getis-Ord Gi*), this study detects the spatially clustered urban and forest areas. We establish a multilevel regression model to explore the relationship between urban and forest areas measured in three dimensions, as well as seasonal temperatures. The study shows that while spatially clustered urban and forest areas have consistent associations with the four seasonal temperatures, urban and forest areas at the city scale have different associations with the seasonal temperature, depending on the season. When spatially clustered urban areas increase by 10 km2, four seasonal temperatures increase by about 0.0016-0.0067 Celsius degree (°C); on the other hand, when spatially clustered forest areas increase by 10 km2, four seasonal temperatures decrease by about 0.0001-0.0016 °C. At the neighborhood level, urban and forest areas are negatively associated with the four seasonal temperatures. The results of this study can be utilized by urban planners and policymakers to establish land use planning or policy by providing evidence of whether land use plans should be established and at what scales to manage regional thermal environments. To alleviate seasonal warming, we recommend increasing forest areas at the neighborhood and spatially clustered levels and controlling the size of spatially clustered urban areas.

Human influence has intensified extreme precipitation in North America.

Precipitation extremes have implications for many facets of both the human and natural systems, predominantly through flooding events. Observations have demonstrated increasing trends in extreme precipitation in North America, and models and theory consistently suggest continued increases with future warming. Here, we address the question of whether observed changes in annual maximum 1- and 5-d precipitation can be attributed to human influence on the climate. Although attribution has been demonstrated for global and hemispheric scales, there are few results for continental and subcontinental scales. We utilize three large ensembles, including simulations from both a fully coupled Earth system model and a regional climate model. We use two different attribution approaches and find many qualitatively consistent results across different methods, different models, and different regional scales. We conclude that external forcing, dominated by human influence, has contributed to the increase in frequency and intensity of regional precipitation extremes in North America. If human emissions continue to increase, North America will see further increases in these extremes.

A climate-change vulnerability and adaptation assessment for Brazil's protected areas.

Brazil hosts the largest expanse of tropical ecosystems within protected areas (PAs), which shelter biodiversity and support traditional human populations. We assessed the vulnerability to climate change of 993 terrestrial and coastal-marine Brazilian PAs by combining indicators of climatic-change hazard with indicators of PA resilience (size, native vegetation cover, and probability of climate-driven vegetation transition). This combination of indicators allows the identification of broad climate-change adaptation pathways. Seventeen PAs (20,611 km2 ) were highly vulnerable and located mainly in the Atlantic Forest (7 PAs), Cerrado (6), and the Amazon (4). Two hundred fifty-eight PAs (756,569 km2 ), located primarily in Amazonia, had a medium vulnerability. In the Amazon and western Cerrado, the projected severe climatic change and probability of climate-driven vegetation transition drove vulnerability up, despite the generally good conservation status of PAs. Over 80% of PAs of high or moderate vulnerability are managed by indigenous populations. Hence, besides the potential risks to biodiversity, the traditional knowledge and livelihoods of the people inhabiting these PAs may be threatened. In at least 870 PAs, primarily in the Atlantic Forest and Amazon, adaptation could happen with little or no intervention due to low climate-change hazard, high resilience status, or both. At least 20 PAs in the Atlantic Forest, Cerrado, and Amazonia should be targeted for stronger interventions (e.g., improvement of ecological connectivity), given their low resilience status. Despite being a first attempt to link vulnerability and adaptation in Brazilian PAs, we suggest that some of the PAs identified as highly or moderately vulnerable should be prioritized for testing potential adaptation strategies in the near future.

Evaluación de la Vulnerabilidad y Adaptación al Cambio Climático de Áreas Protegidas en Brasil Resumen Brasil alberga la mayor extensión de ecosistemas tropicales dentro de áreas protegidas (AP), que protegen la biodiversidad y sustentan a poblaciones humanas tradicionales. Evaluamos la vulnerabilidad al cambio climático de 993 AP brasileñas terrestres y costeras-marinas mediante la combinación de indicadores de riesgo de cambio climático con indicadores de la resiliencia de AP (tamaño, cobertura de vegetación nativa y la probabilidad de transición en la vegetación como consecuencia del cambio climático). Esta combinación de indicadores permite la identificación de amplias rutas de adaptación al cambio climático. Diecisiete AP (20,611 km2 ) fueron altamente vulnerables y se localizaron principalmente en el Bosque Atlántico (7 AP), El Cerrado (6) y la Amazonía (4). Doscientos cincuenta y ocho AP (756,569 km2 ), localizadas principalmente en la Amazonía, tuvieron vulnerabilidad media. En la Amazonía y el oeste de El Cerrado, el severo cambio climático proyectado y la probabilidad de transición de vegetación dirigida por el clima incrementó la vulnerabilidad, a pesar del estado de conservación generalmente bueno de las AP. Más de 80% de las AP con vulnerabilidad alta o media son manejadas por poblaciones indígenas. Por lo tanto, además de los riesgos potenciales para la biodiversidad, también hay amenazas para el conocimiento tradicional y las formas de vida de la gente que habita en esas AP. En por lo menos 870 AP, principalmente en el Bosque Atlántico y la Amazonía, la adaptación podría suceder con poca o ninguna intervención debido al bajo riesgo de cambio climático, estatus de resiliencia alta, o ambos. Por lo menos 20 AP en el Bosque Atlántico, El Cerrado y la Amazonía deberían ser objetivo de intervenciones mayores (e.g., mejoramiento de la conectividad ecológica), dada su estatus de resiliencia baja. A pesar de que es un primer intento para vincular vulnerabilidad y adaptación en AP brasileñas, sugerimos que algunas de las AP identificadas como alta o moderadamente vulnerables se deben priorizar para probar posibles estrategias de adaptación en un futuro próximo.

Climate-driven ground-level ozone extreme in the fall over the Southeast United States.

Ground-level ozone is adverse to human and vegetation health. High ground-level ozone concentrations usually occur over the United States in the summer, often referred to as the ozone season. However, observed monthly mean ozone concentrations in the southeastern United States were higher in October than July in 2010. The October ozone average in 2010 reached that of July in the past three decades (1980-2010). Our analysis shows that this extreme October ozone in 2010 over the Southeast is due in part to a dry and warm weather condition, which enhances photochemical production, air stagnation, and fire emissions. Observational evidence and modeling analysis also indicate that another significant contributor is enhanced emissions of biogenic isoprene, a major ozone precursor, from water-stressed plants under a dry and warm condition. The latter finding is corroborated by recent laboratory and field studies. This climate-induced biogenic control also explains the puzzling fact that the two extremes of high October ozone both occurred in the 2000s when anthropogenic emissions were lower than the 1980s and 1990s, in contrast to the observed decreasing trend of July ozone in the region. The occurrences of a drying and warming fall, projected by climate models, will likely lead to more active photochemistry, enhanced biogenic isoprene and fire emissions, an extension of the ozone season from summer to fall, and an increase of secondary organic aerosols in the Southeast, posing challenges to regional air quality management.

Climate extremes, land-climate feedbacks and land-use forcing at 1.5°C.

This article investigates projected changes in temperature and water cycle extremes at 1.5°C of global warming, and highlights the role of land processes and land-use changes (LUCs) for these projections. We provide new comparisons of changes in climate at 1.5°C versus 2°C based on empirical sampling analyses of transient simulations versus simulations from the 'Half a degree Additional warming, Prognosis and Projected Impacts' (HAPPI) multi-model experiment. The two approaches yield similar overall results regarding changes in climate extremes on land, and reveal a substantial difference in the occurrence of regional extremes at 1.5°C versus 2°C. Land processes mediated through soil moisture feedbacks and land-use forcing play a major role for projected changes in extremes at 1.5°C in most mid-latitude regions, including densely populated areas in North America, Europe and Asia. This has important implications for low-emissions scenarios derived from integrated assessment models (IAMs), which include major LUCs in ambitious mitigation pathways (e.g. associated with increased bioenergy use), but are also shown to differ in the simulated LUC patterns. Biogeophysical effects from LUCs are not considered in the development of IAM scenarios, but play an important role for projected regional changes in climate extremes, and are thus of high relevance for sustainable development pathways.This article is part of the theme issue 'The Paris Agreement: understanding the physical and social challenges for a warming world of 1.5°C above pre-industrial levels'.

Delayed detection of climate mitigation benefits due to climate inertia and variability.

Climate change mitigation acts by reducing greenhouse gas emissions, and thus curbing, or even reversing, the increase in their atmospheric concentration. This reduces the associated anthropogenic radiative forcing, and hence the size of the warming. Because of the inertia and internal variability affecting the climate system and the global carbon cycle, it is unlikely that a reduction in warming would be immediately discernible. Here we use 21st century simulations from the latest ensemble of Earth System Model experiments to investigate and quantify when mitigation becomes clearly discernible. We use one of the scenarios as a reference for a strong mitigation strategy, Representative Concentration Pathway (RCP) 2.6 and compare its outcome with either RCP4.5 or RCP8.5, both of which are less severe mitigation pathways. We analyze global mean atmospheric CO2, and changes in annually and seasonally averaged surface temperature at global and regional scales. For global mean surface temperature, the median detection time of mitigation is about 25-30 y after RCP2.6 emissions depart from the higher emission trajectories. This translates into detection of a mitigation signal by 2035 or 2045, depending on whether the comparison is with RCP8.5 or RCP4.5, respectively. The detection of climate benefits of emission mitigation occurs later at regional scales, with a median detection time between 30 and 45 y after emission paths separate. Requiring a 95% confidence level induces a delay of several decades, bringing detection time toward the end of the 21st century.

Regional climate change scenarios applied to viticultural zoning in Mendoza, Argentina.

Due to the importance of the winemaking sector in Mendoza, Argentina, the assessment of future scenarios for viticulture is of foremost relevance. In this context, it is important to understand how temperature increase and precipitation changes will impact on grapes, because of changes in grapevine phenology and suitability wine-growing regions must be understood as an indicator of climate change. The general objective is to classify the suitable areas of viticulture in Argentina for the current and future climate using the MM5 regional climate change simulations. The spatial distribution of annual mean temperature, annual rainfall, and some bioclimatic indices has been analyzed for the present (1970-1989) and future (2080-2099) climate under SRES A2 emission scenario. In general, according to projected average growing season temperature and Winkler index classification, the regional model estimates (i) a reduction of cool areas, (ii) a westward and southward displacement of intermediate and warm suitability areas, and (iii) the arise of new suitability regions (hot and very hot areas) over Argentina. In addition, an increase of annual accumulated precipitation is projected over the center-west of Argentina. Similar pattern of change is modeled for growing season, but with lower intensity. Furthermore, the evaluation of projected seasonal precipitation shows a little precipitation increase over Cuyo and center of Argentina in summer and a little precipitation decrease over Cuyo and northern Patagonia in winter. Results show that Argentina has a great potential for expansion into new suitable vineyard areas by the end of twenty-first century, particularly due to projected displacement to higher latitudes for most present suitability winegrowing regions. Even though main conclusions are based on one global-regional model downscaling, this approach provides valuable information for implementing proper and diverse adaptation measures in the Argentinean viticultural regions. It has been concluded that regional climate change simulations are an adequate methodology, and indeed, the MM5 regional model is an appropriate tool to be applied in viticultural zoning in Mendoza, Argentina.

Incremental dynamical downscaling for probabilistic analysis based on multiple GCM projections.

A dynamical downscaling method for probabilistic regional-scale climate change projections was developed to cover the inherent uncertainty associated with multiple general circulation model (GCM) climate simulations. The climatological increments estimated by GCM results were statistically analyzed using the singular vector decomposition. Both positive and negative perturbations from the ensemble mean with the magnitudes of their standard deviations were extracted and added to the ensemble mean of the climatological increments. The analyzed multiple modal increments were utilized to create multiple modal lateral boundary conditions for the future climate regional climate model (RCM) simulations by adding them to reanalysis data. The incremental handling of GCM simulations realized approximated probabilistic climate change projections with the smaller number of RCM simulations. For the probabilistic analysis, three values of a climatological variable simulated by RCMs for a mode were analyzed under an assumption of linear response to the multiple modal perturbations.

Assessing climate change impacts on the rape stem weevil, Ceutorhynchus napi Gyll., based on bias- and non-bias-corrected regional climate change projections.

Agricultural production is directly affected by projected increases in air temperature and changes in precipitation. A multi-model ensemble of regional climate change projections indicated shifts towards higher air temperatures and changing precipitation patterns during the summer and winter seasons up to the year 2100 for the region of Goettingen (Lower Saxony, Germany). A second major controlling factor of the agricultural production is the infestation level by pests. Based on long-term field surveys and meteorological observations, a calibration of an existing model describing the migration of the pest insect Ceutorhynchus napi was possible. To assess the impacts of climate on pests under projected changing environmental conditions, we combined the results of regional climate models with the phenological model to describe the crop invasion of this species. In order to reduce systematic differences between the output of the regional climate models and observational data sets, two different bias correction methods were applied: a linear correction for air temperature and a quantile mapping approach for precipitation. Only the results derived from the bias-corrected output of the regional climate models showed satisfying results. An earlier onset, as well as a prolongation of the possible time window for the immigration of Ceutorhynchus napi, was projected by the majority of the ensemble members.

Bio-geophysical feedback to climate caused by the conversion of Amazon Forest to soybean plantations.

Over the past two decades, soybean cultivation has become one of the principal replacements for forests in the Brazilian Amazon. Previous studies showed that the conversion of forests into large-scale soybean farms has different effects on local and regional climate than other forms of land use, e.g., conversion to pasture. The bio-geophysical feedbacks that lead to changes in temperature and rainfall caused by the expansion of commodity crops is not fully understood, and this has implications for both modelling potential future climatic change and understanding its impact. Here we performed model simulations to characterize the feedback to climate caused by the replacement of Amazonian forests with soybean and pastures. Our results show that: when compared to deforestation caused by pastures, the conversion of forests into soybean plantations results in more pronounced changes in the atmospheric boundary layer. Because they are characterized by a period of the year with bare soil, soybean fields transmit more long-wave radiation to the atmosphere than pastures, leading to an increase in boundary layer average temperature by 2.4 K. Although soybean plantations tend to strengthen convective lifting, the decrease in boundary layer water vapor content plays a decisive role in reducing rainfall. Finally, we demonstrate that the climatic impacts associated with the replacement of forests by soybean is likely to be magnified with agricultural expansion along new frontiers in the northern and western regions of the Amazon basin due to a more pronounced reduction in water vapor content.

Predicting the magnitude and the characteristics of the urban heat island in coastal cities in the proximity of desert landforms. The case of Sydney.

The urban heat island is a vastly documented climatological phenomenon, but when it comes to coastal cities, close to desert areas, its analysis becomes extremely challenging, given the high temporal variability and spatial heterogeneity. The strong dependency on the synoptic weather conditions, rather than on city-specific, constant features, hinders the identification of recurrent patterns, leading conventional predicting algorithms to fail. In this paper, an advanced artificial intelligence technique based on long short-term memory (LSTM) model is applied to gain insight and predict the highly fluctuating heat island intensity (UHII) in the city of Sydney, Australia, governed by the dualistic system of cool sea breeze from the ocean and hot western winds from the vast desert biome inlands. Hourly measurements of temperature, collected for a period of 18 years (1999-2017) from 8 different sites in a 50 km radius from the coastline, were used to train (80%) and test (20%) the model. Other inputs included date, time, and previously computed UHII, feedbacked to the model with an optimized time step of six hours. A second set of models integrated wind speed at the reference station to account for the sea breeze effect. The R2 ranged between 0.770 and 0.932 for the training dataset and between 0.841 and 0.924 for the testing dataset, with the best performance attained right in correspondence of the city hot spots. Unexpectedly, very little benefit (0.06-0.43%) was achieved by including the sea breeze among the input variables. Overall, this study is insightful of a rather rare climatological case at the watershed between maritime and desertic typicality. We proved that accurate UHII predictions can be achieved by learning from long-term air temperature records, provided that an appropriate predicting architecture is utilized.

Trends in atmospheric patterns conducive to seasonal precipitation and temperature extremes in California.

Recent evidence suggests that changes in atmospheric circulation have altered the probability of extreme climate events in the Northern Hemisphere. We investigate northeastern Pacific atmospheric circulation patterns that have historically (1949-2015) been associated with cool-season (October-May) precipitation and temperature extremes in California. We identify changes in occurrence of atmospheric circulation patterns by measuring the similarity of the cool-season atmospheric configuration that occurred in each year of the 1949-2015 period with the configuration that occurred during each of the five driest, wettest, warmest, and coolest years. Our analysis detects statistically significant changes in the occurrence of atmospheric patterns associated with seasonal precipitation and temperature extremes. We also find a robust increase in the magnitude and subseasonal persistence of the cool-season West Coast ridge, resulting in an amplification of the background state. Changes in both seasonal mean and extreme event configurations appear to be caused by a combination of spatially nonuniform thermal expansion of the atmosphere and reinforcing trends in the pattern of sea level pressure. In particular, both thermal expansion and sea level pressure trends contribute to a notable increase in anomalous northeastern Pacific ridging patterns similar to that observed during the 2012-2015 California drought. Collectively, our empirical findings suggest that the frequency of atmospheric conditions like those during California's most severely dry and hot years has increased in recent decades, but not necessarily at the expense of patterns associated with extremely wet years.

Improved hydrological model parametrization for climate change impact assessment under data scarcity - The potential of field monitoring techniques and geostatistics.

According to current climate projections, Mediterranean countries are at high risk for an even pronounced susceptibility to changes in the hydrological budget and extremes. These changes are expected to have severe direct impacts on the management of water resources, agricultural productivity and drinking water supply. Current projections of future hydrological change, based on regional climate model results and subsequent hydrological modeling schemes, are very uncertain and poorly validated. The Rio Mannu di San Sperate Basin, located in Sardinia, Italy, is one test site of the CLIMB project. The Water Simulation Model (WaSiM) was set up to model current and future hydrological conditions. The availability of measured meteorological and hydrological data is poor as it is common for many Mediterranean catchments. In this study we conducted a soil sampling campaign in the Rio Mannu catchment. We tested different deterministic and hybrid geostatistical interpolation methods on soil textures and tested the performance of the applied models. We calculated a new soil texture map based on the best prediction method. The soil model in WaSiM was set up with the improved new soil information. The simulation results were compared to standard soil parametrization. WaSiMs was validated with spatial evapotranspiration rates using the triangle method (Jiang and Islam, 1999). WaSiM was driven with the meteorological forcing taken from 4 different ENSEMBLES climate projections for a reference (1971-2000) and a future (2041-2070) times series. The climate change impact was assessed based on differences between reference and future time series. The simulated results show a reduction of all hydrological quantities in the future in the spring season. Furthermore simulation results reveal an earlier onset of dry conditions in the catchment. We show that a solid soil model setup based on short-term field measurements can improve long-term modeling results, which is especially important in ungauged catchments.