Seasonal Mean Temperature Research Articles

Evaluation of CORDEX- South Asia regional climate models for heat wave simulations over India

The episodes of heat wave events have strengthened in recent decades causing great concern for human health, agriculture and natural ecosystem. In the present study, Regional Climate Models (RCMs) namely, CCAM and RegCM, from Coordinated Regional Climate Downscaling Experiments (CORDEX) for South Asia (SA) are evaluated for simulating heat waves (March–June) for a long-term period (1971 to 2005) over India in comparison with observations from India Meteorological Department (IMD). The statistical analysis (correlation, RMSE, MAE, ECDF) results reveal differences in RCMs in simulating spatial pattern and trends of maximum temperature before bias correction. Variance scaling bias correction is found to remove bias and improve model simulations in capturing temperature variability. An increase in correlation in daily observations from 0.24 to 0.70 and reduction in RMSE from 8.08 °C to 2.02 °C and MAE from 3.87 °C to 2.43 °C after bias correction is observed between model and observation.LMDZ4 and GFDL-ESM2M are found to perform best in simulating interannual variability of seasonal mean maximum temperature with an underestimation of −7.74% and −15.41% which improved significantly to around −1.51% and − 0.78%, respectively after bias correction over India. LMDZ4 and GFDL-ESM2M are also best-performing models in significantly reproducing the heat wave frequency and spatial variability in closer proximity with observations over India amongst all models after bias correction. Over NW and western regions, the LMDZ4 and GFDL-ESM2M ensemble models successfully capture the increasing trend of 0.2 events/year and 0.4 events/year accordance to IMD and IITM criteria, respectively. However, the ACCESS1.0, CNRM-CM5 and CCSM4 ensemble experiments overestimated heat waves by ±40 events in most sub-divisions in India. Over the central Indian regions, the ACCESS 1.0 and CNRM-CM5 model output show a negative trend of −0.2 events/year and large spatial variability possibly due to model associated uncertainties. Overall the results show an improvement in capturing maximum temperature and heat waves across the regions of Indian sub-continent in the bias-corrected downscaled CORDEX-SA ensemble RCMs than without bias-corrected output. The study suggests a way forward to assess RCMs performance and uncertainty in extreme weather analysis in future projections.

ANALYSIS OF ANNUAL AND SEASONAL AIR TEMPERATURE TRENDS IN CENTRAL PART OF ROMANIA

In the present study, the research was focused on analyzing long-term trends and variability of annual and seasonal air mean temperatures and some parameters related to cold and hot extreme temperatures, based on relevant continuous temperature data (1961-2018 period) provided by three regional weather stations (Cluj-Napoca, Deva and Miercurea-Ciuc). The Mann-Kendall test was used in order to find statistically-significant trends, while the magnitude of trends was estimated using Sen’s slope estimator method. The main results showed a significant increase of mean air temperature, especially in the summer season and for indicators related to hot extreme temperatures. Generally, our findings are consistent with the current global warming and previous study results on temperature variability in Romania. Anyway by comparing our results with those from the other similar studies performed in Romania, it could be observed that the climate became warmer in the study area, so the trend in annual mean and seasonal air temperature has intensified and statistical significance of the results has significantly increased. Also, the intensification of the ,,urban heat island’’ effect could be highlighted in the larger cities, like Cluj-Napoca. This increasing trend entails significant impacts on society, economy and natural ecosystems, which is expected to intensify in the near future.

Microclimate and crop performance in a tunnel greenhouse shaded by organic photovoltaic modules – Comparison with conventional shaded and unshaded tunnels

The use of flexible and semi-transparent organic photovoltaic (OPV) modules as shading elements in a greenhouse tunnel with a tomato crop is presented. Experiments were performed in two similar greenhouse tunnels, covered by diffuse polyethylene sheet, during two summer growing seasons. In 2018, one tunnel was shaded using OPV modules (covering 37% of the roof area and resulting in 23% shading) and the second tunnel served as a control. In 2019, a 25% black shading screen was added to the control tunnel. The microclimate, yield, and physiological parameters were examined in the two tunnels. Results show that at noon (11:00 to 13:00), there was no significant difference in the mean seasonal (June–September 2018, May–August 2019) air temperature and humidity between the tunnels. In 2018, the tunnels differed in terms of the spatial radiation transmittance and leaf temperature. The average radiation level along the OPV tunnel centreline was much lower, and the radiation distribution was less homogeneous than in the control. In 2019, with similar shading percentages in the tunnels, similar average radiation levels were observed. The leaf temperature in the OPV was lower than in the control in 2018 and varied from higher to lower in 2019. The leaf area index (LAI), cumulative yield, and average fruit mass were higher in the OPV than in the control in 2018, and similar in 2019. The average value of the maximum power output of three OPV modules increased roughly linearly with irradiance.

An investigation of hibernating members from the Culex pipiens complex (Diptera, Culicidae) in subterranean habitats of central Germany

The Culex pipiens complex encompasses five species and subspecies of the genus Culex. Over time, a multitude of morphologically indistinguishable species has been assigned to this complex with several species being classified as important vectors for different diseases. Some species of this complex hibernate in subterranean habitats, and it has been proven that viruses can survive this phase of hibernation. However, studies focusing on the environmental requirements, ecology and spatial and temporal distribution patterns of mosquitos in underground habitats are sparse. Here, we investigate the main environmental factors and dependencies of Culex, considering the number of individuals and survival probabilities in underground habitats during the winter months. Methods. Since the State of Hesse, Germany harbors about 3500 to 4000 subterranean shelters ample availability of subterranean habitats there provides a good opportunity to conduct detailed investigations of the Culex pipiens complex. In this study, we identified a sample of 727 specimens of overwintering females within the Culex pipiens complex from 52 different underground sites collected over a period of 23 years using qPCR. A complete data set of samplings of hibernating mosquitos from 698 subterranean habitats in Central Germany over the same period was available to study the spatial and temporal patterns and the effect of temperature and precipitation conditions on these hibernating populations using a generalized linear model (GLM). Results. Our qPCR-results show, similar to aboveground studies of mosquitos, that Culex pipiens pipiens and Culex torrentium occur sympatrically. On the other hand, Culex pipiens molestus occurred very rarely. The GLM revealed no shifts in species composition over time, but different preferences for subterranean hibernacula, chemical effects on overwintering populations as well as effects of annual and seasonal mean temperature and precipitation during the active phase from March to November. Cx. p. pipiens and Cx. torrentium are the most common species within Hessian caves and other underground habitats during winter. They co-occur with different frequency without any patterns in species composition. Weather conditions influence the number of overwintering mosquitos during the activity phase. Depending on cave parameters, the number of mosquitos decreases during the winter months.

To what extent does the detection of climate change in Hungary depend on the choice of statistical methods?

The trend analysis of meteorological time series has gained prominence in recent decades, the most common method being the so-called ‘linear analytical trend analysis’. Until the mid-1990s, trend analysis was commonly performed on non-homogenized data sets, which frequently led to erroneous conclusions. Nowadays, only homogenized data sets are examined, so it really is possible to detect climate change in long meteorological data sets. In this paper, the methodology of linear trend analysis is summarized, the way in which the model can be validated is demonstrated, and there is a discussion of the results obtained if unjustified discontinuities caused by changing measurement conditions, such as the relocation of stations, changes in measurement time, or instrument change occur. On the basis of an examination of records for the preceding 118 years, it is possible to state that both annual and seasonal mean temperature trends display a significant warming trend. In the case of homogenized data series, the change is significant over the entire territory of Hungary; in the case of raw data series, however, the change is not significant everywhere. The validity of the linear model is tested using the F-test, a task as yet carried out on the entire Hungarian data series, series comprising records for over 100 years. Furthermore, neither has a comparison been made of the trend data for raw data series and the homogenized data series with the help of information on station history to explore the causes of inhomogeneity.

Sub micron aerosol variability and its ageing process at a high altitude site in India: Impact of meteorological conditions

The effect of relative humidity and temperature on the submicron aerosol variability and its ageing process was studied over a high altitude site, Mahabaleshwar in south-west India. The mass composition of non-refractory particulate matter of 1 μm (NR-PM1) size was obtained using Time of Flight Aerosol Chemical Speciation Monitor (ToF-ACSM) along with the measurements on a few trace gases during winter (December 2017–February 2018) and summer season (20th March - 5th May 2018). Sulfate exhibited strong dependence on the relative humidity (RH) as its mass fraction increased with the increase in RH. The Sulfate oxidation ratio (SOR) calculated during summer season also showed an increasing trend with RH indicating the influence of aqueous phase oxidation on sulfate fraction. On the other hand, OOA showed remarkable enhancement in its mass fraction with the increase in temperature along with the corresponding increase in f44 and tropospheric ozone. OOA, ozone and f44 ratio increased 14–34%, 8–26% and 25–43% respectively with the increase in temperature from 18 to 30 °C. This is indicative of the dominance of photochemical ageing processes during high temperature conditions. The extent of photochemical ageing was found to be higher during summer season (mean temperature ∼25.4 ± 2.6 °C) as compared to winter season (mean temperature ∼20.5 ± 2.6 °C). The nitrate diurnal was majorly governed by gas to particle partitioning process during winter season, whereas the summertime nitrate diurnal was influenced primarily by its formation rate. The non parametric wind regression analysis revealed that the mass concentration during winter was majorly contributed by distant sources from north east direction while during summer the local sources were more dominant.

Analysis on the Characteristics of Temperature Change in Zhengzhou from 1990 to 2017

In order to find out the changing trend of temperature and extreme weather in Zhengzhou under the background of rising global temperature. This study analyzes the changing trend of annual mean temperature and seasonal average temperature in Zhengzhou from 1990 to 2017 based on meteorological data. The extreme temperature index for 28 years was extracted. The results show that from 1990 to 2017, the air temperature in Zhengzhou shows a significant upward trend, and its rising rate is higher than that of the global warming rate. There are differences in the four seasons in Zhengzhou, with the largest change rate in spring, followed by summer and autumn, and the weakest warming trend in winter. The extreme temperature index in Zhengzhou has certain regularity, the extreme index FD0 decreases significantly, the change of ID0 is not obvious, and SU25, TR20 and HTD35 increase significantly. Except for the change of IDO is not obvious, other indexes can better reflect the warming trend of temperature in Zhengzhou, indicating that the extreme temperature index has an obvious response to the change of temperature and can be used as an index to study the change of urban temperature.

A Third Generation of Homogenized Temperature for Trend Analysis and Monitoring Changes in Canada’s Climate

ABSTRACT This study presents the development of a new dataset of homogenized temperature for use in trend analysis and monitoring climate change in Canada. This dataset contains daily data for 780 locations across the country: 508 locations with an active station (current observations) and long record (starting prior to 1990); 53 locations with an active station and short record (starting after 1990); and 219 locations with no current observations (station closed) but with more than 30 years of data. Daily observations from nearby sites were often merged into a single record to create a long time series. This new dataset includes observations taken at Reference Climate Stations and from the Canada Aviation Weather Services, which are used to extend past climate observations into recent times. First, the data were quality controlled. The daily minimum temperature was adjusted for the change in observing time at principal stations in 1961. Parallel daily data were used to detect non-climatic shifts when the observations from nearby sites were merged. Series of annual and seasonal mean temperatures were tested for homogeneity. Daily temperatures were adjusted using a quantile-matching procedure if needed. Two main causes of data inhomogeneity affecting the trends over the 1948–2018 and 1900–2018 periods were identified. First, the change in observing time in 1961 introduced a cold bias in the annual means of the daily minimum temperatures after 1961. Second, merging observations from airport stations with older records has often created an artificial decreasing shift in the unadjusted data because of the better exposure of the instruments at airport stations. This new homogenized dataset shows a slightly stronger warming than the unadjusted data: the trend in the annual mean temperature for Canada has changed from 1.69° to 1.74°C for 1948–2018, and the trend for southern Canada has changed from 1.32° to 1.62°C for 1900–2018 because of all the adjustments applied to daily temperature in this study.

Why don't phenophase dates in the current year affect the same phenophase dates in the following year?

Examining whether a phenophase occurrence date in the current year affects the same phenophase occurrence date in the following year is crucial for developing cross-year phenological prediction models. Here, we carried out correlation analyses between leaf unfolding start (LUS)/leaf fall end (LFE) dates in the current and following years for four dominant tree species in temperate northern China from 1981 to 2012. Then, we calculated the recurrence intervals of LUS and LFE between two adjacent years for each species. Moreover, we investigated temperature effects on LUS/LFE dates, growing season and non-growing season lengths. Results show that correlation coefficients between LUS/LFE dates in the current and following years are nonsignificant at most stations. The recurrence interval of a phenophase has slight interannual variation and correlates significantly (and negatively) with the phenophase occurrence date of the current year. Further analyses indicate that LUS dates correlate significantly (and negatively) with spring mean temperatures, while LFE dates correlate significantly (and positively) with autumn mean temperatures, but negatively with growing season mean temperatures. In addition, spring mean temperatures can influence growing season length by controlling LUS date but cannot influence the following non-growing season length. Similarly, autumn mean temperatures and growing season mean temperatures can influence the subsequent non-growing season length but cannot influence the growing season length of the following year. Our study highlights that recurrence interval and time restrictions in the effects of seasonal temperatures on phenophase dates are the main environmental causes of nonsignificant correlations between phenophase occurrence dates in the current and following years.

Simulation of Possible Future Climate Changes in the 21st Century in the INM-CM5 Climate Model

Climate changes in 2015–2100 have been simulated with the use of the INM-CM5 climate model following four scenarios: SSP1-2.6, SSP2-4.5, and SSP5-8.5 (single model runs) and SSP3-7.0 (an ensemble of five model runs). Changes in the global mean temperature and spatial distribution of temperature and precipitation are analyzed. The global warming predicted by the INM-CM5 model in the scenarios considered is smaller than that in other CMIP6 models. It is shown that the temperature in the hottest summer month can rise more quickly than the seasonal mean temperature in Russia. An analysis of a change in Arctic sea ice shows no complete Arctic summer ice melting in the 21st century under any model scenario. Changes in the meridional streamfunction in atmosphere and ocean are studied.

Skillful seasonal prediction of key carbon cycle components: NPP and fire risk

We investigate the skill of the GloSea5 seasonal forecasting system for two carbon cycle processes, which are strong contributors to global CO2 variability: the impact of meteorological conditions on CO2 uptake by vegetation (characterised by net primary productivity, NPP), and on fire occurrences (characterised by fire risk indices). Current seasonal forecasts of global CO2 concentrations rely on the relationship with the El Niño–Southern Oscillation (ENSO), combined with estimated anthropogenic emissions. NPP and fire are key processes underlying that global CO2–ENSO relationship: In the tropics, during El Niño events, CO2 uptake by vegetation is reduced and fires occur more frequently, leading to higher global CO2 levels. Our study assesses the skill of these processes in the forecast model for the first time. We use the McArthur forest fire index, calculated from daily data from several meteorological variables. We also assess a simpler fire index, based solely on seasonal mean temperature and relative humidity, to test the need for additional complexity. For NPP, the skill is high in regions that respond strongly to ENSO, such as equatorial South America in boreal winter, and northeast Brazil in boreal summer. There is also skill in some regions without a strong ENSO response. The fire risk indices show significant skill across much of the tropics, including Indonesia, southern and eastern Africa, and parts of the Amazon. We relate this skill to the underlying meteorological variables, finding that fire risk in particular follows similar patterns to relative humidity. On the seasonal-mean timescale, the McArthur index offers no benefits over the simpler fire index: they show the same relationship to burnt area and response to ENSO, and the same levels of skill, in almost all cases. Our results highlight potentially useful prediction skill, as well as important limitations, for seasonal forecasts of land-surface impacts of climate variability.

Partitioning uncertainty components of mean climate and climate change in a large ensemble of European regional climate model projections

A study of seasonal mean temperature, precipitation, and wind speed has been performed for a set of 19 global climate model (GCM) driven high-resolution regional climate model (RCM) simulations forming a complete 5 × 4 GCM × RCM matrix with only one missing simulation. Differences between single simulations and between groups of simulations forced by a specific GCM or a specific RCM are identified. With the help of an analysis of variance (ANOVA) we split the ensemble variance into linear GCM and RCM contributions and cross terms for both mean climate and climate change for the end of the current century according to the RCP8.5 emission scenario. The results document that the choice of GCM generally has a larger influence on the climate change signal than the choice of RCM, having a significant influence for roughly twice as many points in the area for the fields investigated (temperature, precipitation and wind speed). It is also clear that the RCM influence is generally concentrated close to the eastern and northern boundaries and in mountainous areas, i.e., in areas where the added surface detail of e.g. orography, snow and ice seen by the RCM is expected to have considerable influence on the climate, and in areas where the air in general has spent the most time within the regional domain. The analysis results in estimates of areas where the specific identity of either GCM or RCM is formally significant, hence obtaining an indication about regions, seasons, and fields where linear superpositions of GCM and RCM effects are good approximations to an actual simulation for both the mean fields analysed and their changes. In cases where linear superposition works well, the frequently encountered sparse GCM–RCM matrices may be filled with emulated results, leading to the possibility of giving more fair relative weight between model simulations than simple averaging of existing simulations. An important result of the present study is that properties of the specific GCM–RCM combination are generally important for the mean climate, but negligible for climate change for the seasonal-mean surface fields investigated here.

A global database of Holocene paleotemperature records

A comprehensive database of paleoclimate records is needed to place recent warming into the longer-term context of natural climate variability. We present a global compilation of quality-controlled, published, temperature-sensitive proxy records extending back 12,000 years through the Holocene. Data were compiled from 679 sites where time series cover at least 4000 years, are resolved at sub-millennial scale (median spacing of 400 years or finer) and have at least one age control point every 3000 years, with cut-off values slackened in data-sparse regions. The data derive from lake sediment (51%), marine sediment (31%), peat (11%), glacier ice (3%), and other natural archives. The database contains 1319 records, including 157 from the Southern Hemisphere. The multi-proxy database comprises paleotemperature time series based on ecological assemblages, as well as biophysical and geochemical indicators that reflect mean annual or seasonal temperatures, as encoded in the database. This database can be used to reconstruct the spatiotemporal evolution of Holocene temperature at global to regional scales, and is publicly available in Linked Paleo Data (LiPD) format.

Intertidal gobies acclimate rate of luminance change for background matching with shifts in seasonal temperature.

Rate of colour change and background matching capacity are important functional traits for avoiding predation and hiding from prey. Acute changes in environmental temperature are known to impact the rate at which animals change colour, and therefore may affect their survival. Many ectotherms have the ability to acclimate performance traits such as locomotion, metabolic rate and growth rate with changes in seasonal temperature. However, it remains unclear how other functional traits that are directly linked to behaviour and survival respond to long-term changes in temperature (within an individual's lifetime). We assessed whether the rate of colour change is altered by long-term changes in temperature (seasonal variation) and if rate of colour change can acclimate to seasonal thermal conditions. We used an intertidal rock-pool goby Bathygobius cocosensis, to test this and exposed individuals to representative seasonal mean temperatures (16 or 31°C, herein referred to cold- and warm-exposed fish respectively) for 9weeks and then tested their rate of luminance change when placed on white and black backgrounds at acute test temperatures 16 and 31°C. We modelled rate of luminance change using the visual sensitives of a coral trout Plectropmus leopardus to determine how well gobies matched their backgrounds in terms of luminance contrast to a potential predator. After exposure to long-term seasonal conditions, the warm-exposed fish had faster rates of luminance change and matched their background more closely when tested at 31 than at 16°C. Similarly, the cold-exposed fish had faster rates of luminance change and matched their backgrounds more closely at 16°C than at 31°C. This demonstrates that rate of luminance change can be adjusted to compensate for long-term changes in seasonal temperature. This is the first study to show that animals can acclimate rate of colour change for background matching to seasonal thermal conditions. We also show that rapid changes in acute temperature reduce background matching capabilities. Stochastic changes in climate are likely to affect the frequency of predator-prey interactions which may have substantial knock-on effects throughout ecosystems.

Temperature‐body size responses in insects: a case study of British Odonata

1. Body size is highly correlated with physiological traits, fitness, and trophic interactions. These traits are subject to change if there are widespread reductions of body size with warming temperatures, which is suggested as one of the ‘universal’ ecological responses to climate change. However, general patterns of body size response to temperature in insects have not yet emerged.2. To address this knowledge gap, we paired the wing length (as a proxy for body size) of 5331 museum specimens of 14 species of British Odonata with historical temperature data. Three sets of analyses were performed: (i) a regression analysis to test for a relationship between wing length and mean seasonal temperature within species and subsequent comparisons across species and suborders; (ii) an investigation of whether the body size of species has an effect on sensitivity to warming temperature; and (iii) a linear‐mixed effects model to investigate factors that potentially affect temperature–size response.3. The regression analysis indicated that wing length is negatively correlated with mean seasonal temperatures for Zygoptera, whereas Anisoptera showed no significant correlation with temperature.4. There is a significant decline in wing length of all Zygoptera (but not Anisoptera) with collection date, suggesting that individuals emerging later in the season are smaller.5. Life‐cycle type was not important for predicting wing length–temperature responses, whereas sex, species, and suborder were indicated as important factors affecting the magnitude of temperature–size responses in Odonata.6. Overall, wing lengths of Zygoptera are more sensitive to temperature and collection date than Anisoptera.

Population ecology and spatial synchrony in the abundance of leaf gall wasps within and among populations of valley oak (Quercus lobata)

AbstractWhat factors drive population variability through space and time? Here we assess patterns of abundance of seven species of gall wasps in three genera occurring on the leaves of valley oaks (Quercus lobata) at 10 sites throughout this species' statewide range in California, from 2000 to 2006. Our primary goals were to understand the factors driving variability in gall abundance and to assess the extent of spatial synchrony in gall wasp communities at both large and small geographic scales. On the large, statewide scale, there was significant site‐to‐site variation in gall abundance, driven in all cases primarily by differences in mean maximum seasonal temperatures, and lesser year‐to‐year variation. In contrast, on the small, local scale, differences were more pronounced from year to year than from tree to tree, and were to some extent correlated with differences in acorn production, suggesting an interaction with the reproductive effort of hosts. Significant spatial synchrony was detected, particularly at the statewide scale, but in no case did synchrony decline significantly with distance, despite sites being up to 741 km apart. Variation in spatial synchrony was correlated with a number of exogenous factors, including seasonal weather conditions, the acorn crop at the statewide scale and soil phosphorus availability at the local scale; however, most variation in spatial synchrony in our analyses remained unexplained.

Amplified warming of seasonal cold extremes relative to the mean in the Northern Hemisphere extratropics

Abstract. Cold extremes are anticipated to warm at a faster rate than both hot extremes and average temperatures for much of the Northern Hemisphere. Anomalously warm cold extremes can affect numerous sectors, including human health, tourism and various ecosystems that are sensitive to cold temperatures. Using a selection of global climate models, this paper explores the accelerated warming of seasonal cold extremes relative to seasonal mean temperatures in the Northern Hemisphere extratropics. The potential driving physical mechanisms are investigated by assessing conditions on or prior to the day when the cold extreme occurs to understand how the different environmental fields are related. During winter, North America, Europe and much of Eurasia show amplified warming of cold extremes projected for the late 21st century, compared to the mid-20th century. This is shown to be largely driven by reductions in cold air temperature advection, suggested as a likely consequence of Arctic amplification. In spring and autumn, cold extremes are expected to warm faster than average temperatures for most of the Northern Hemisphere mid-latitudes to high latitudes, particularly Alaska, northern Canada and northern Eurasia. In the shoulder seasons, projected decreases in snow cover and associated reductions in surface albedo are suggested as the largest contributor affecting the accelerated rates of warming in cold extremes. The key findings of this study improve our understanding of the environmental conditions that contribute to the accelerated warming of cold extremes relative to mean temperatures.

Investigating Indian summer monsoon in coupled regional land–atmosphere downscaling experiments using RegCM4

The ability of Regional Climate Model (RCM: RegCM4) forced with two different Global Climate Model (GCM: CCSM4 and MIROC5) and three land-surface parameterization (LSP) (i.e., BATS, CLM4.5 and Subgrid-BATS) in simulating the Indian summer monsoon (ISM) is tested for the present climate (1975–2005). Thus six simulation combinations are assessed for seasonal mean temperature, precipitation, and low-level wind for ISM season (June, July, August, September: JJAS) over the COordinated Regional climate Downscaling EXperiment-South Asia (CORDEX-SA) domain. The simulations are evaluated in terms of Taylor’s metric (for precipitation, temperature, zonal wind, meridional wind and total cloud fraction), mean annual cycle, index of agreement, normalized root mean squared deviation and probability distribution function. The experiments simulated moderate events more accurately than high-intensity precipitation events compared to the corresponding observations. The inherent biases in the model simulations are attributed to the weaker meridional wind along with restrained vertical motion during ISM, especially with CCSM4 forcing. A careful analysis of tropospheric temperature gradient (TTG) suggests a weaker north–south (N–S) gradient due to the warmer atmospheric column in these experiments. On the contrary, the MIROC5_CLM4.5 experiment captures the magnitude and the temporal evolution of TTG better during ISM. It also represents the mean features of ISM better than other experiments. Also, the CLM4.5 LSP shows promising performance in ISM simulation when forced with MIROC5. It also provides further avenues for testing of the same combination under different frameworks, including the intended future climate study. This study emphasizes the importance of using appropriate GCM and LSP forcings to the RCM for simulating a coupled complex systems such as ISM.

Contrasting regional and global climate simulations over South Asia

Two ensembles of climate simulations, one global and one regional, are used to investigate model errors and projected climate change in seasonal mean temperature and precipitation over South Asia. The global ensemble includes ten global climate models (GCMs). In the regional ensemble all ten GCMs are downscaled by a regional climate model—RCA4 over South Asia at 50 km resolution. Our focus is on the Indian Summer Monsoon season (June–August) and we show that RCA4 can reproduce, reduce or amplify large-scale GCM biases depending on regions and GCMs. However, the RCA4 bias pattern in precipitation is similar across the simulations, regardless of forcing GCM, indicating a strong RCA4 imprint on the simulated precipitation. For climate change, the results indicate, that RCA4 can change the signal projected by the GCM ensemble and its individual members. There are a few RCA4 simulations with a substantial reduction of projected warming by RCA4 compared to the driving GCMs and with a large regional increase in precipitation absent in the GCMs. We also found that in a number of subregions warm RCA4 biases are related to stronger warming and vice versa, while there is no such dependency in the GCM ensemble. Neither the GCM nor the RCA4 ensemble shows any significant dependency between projected changes and biases for precipitation. Our results implicate that using only RCMs and excluding GCMs, a commonly established approach, can significantly change the message on future regional climate change.

Microclimatic effects on alpine plant communities and flower-visitor interactions

High-alpine ecosystems are commonly assumed to be particularly endangered by climate warming. Recent research, however, suggests that the heterogeneous topography of alpine landscapes provide microclimatic niches for alpine plants (i.e. soil temperatures that support the establishment and reproduction of species). Whether the microclimatic heterogeneity also affects diversity or species interactions on higher trophic levels remains unknown. Here we show that variation in mean seasonal soil temperature within an alpine pasture is within the same range as in plots differing in nearly 500 m in elevation. This pronounced heterogeneity of soil temperature among plots affected the spatial distribution of flowering plant species in our study area with a higher plant richness and cover in warmer plots. This increased plant productivity in warmer plots positively affected richness of flower visitor taxa as well as interaction frequency. Additionally, flower-visitor networks were more generalized in plots with higher plant cover. These results suggest that soil temperature directly affects plant diversity and productivity and indirectly affects network stability. The strong effect of heterogeneous soil temperature on plant communities and their interaction partners may also mitigate climate warming impacts by enabling plants to track their suitable temperature niches within a confined area.