Climate Changes During the Holocene Recorded by Lakes from Europe

Climate change has increased global mean surface temperatures and altered hydrological processes, and projections suggest that these changes will accelerate. As seasonal precipitation patterns change, so will the soil resources available for plants. In the midwestern United States, winter temperatures and precipitation are expected to increase, while snowfall is expected to be reduced. Reduced snowpack could lead to greater frost damage and alter the timing and amount of plant available resources at the start of the growing season. In the summer, precipitation is expected to decrease, and variability is expected to increase, creating longer and more frequent dry periods. In temperate forests, herbaceous understory plants and woody plants in early developmental stages are expected to be highly sensitive to changes in abiotic conditions. Here, we study how seasonal changes in precipitation affect the timing and availability of resources in a temperate deciduous forest. Further, we examine how changes in abiotic conditions influence understory composition and woody plant recruitment. We established a fully factorial experiment that manipulated winter snowfall and summer precipitation to create wet, dry, and control (ambient) conditions in a temperate deciduous forest near West Lafayette, Indiana, USA. We found that large changes in winter and summer precipitation appeared to affect forest processes independently of one another, and changes in seasonal precipitation altered understory composition minimally and had little to no effect on mineralization rates. The recruitment of woody plant species may be more sensitive to altered precipitation, as snow removal lowered germination rates and wet summer conditions lowered relative growth of a woody plant species, Lindera benzoin. In general, though, ecological processes in this forest understory were relatively resistant to change, at least in the short timeframe of this experiment.

Water resource management increasingly seeks to incorporate the impacts of global climate change in the planning of future water supplies. Studies across numerous regions have documented the sensitivity of streamflow to climate change using the precipitation elasticity of streamflow, defined as the proportional change in streamflow to a change in precipitation. This study shows that calculating precipitation elasticity of streamflow without first assessing the strength of the precipitation-streamflow relationship can produce misleading and unrealistic results. For example, the Spokane river basin precipitation elasticity of streamflow is 0.90 by using annual precipitation and streamflow accumulated on a January-December calendar year basis. This indicates that a 10% change in annual precipitation would produce an annual streamflow change of 9.0%. However, using water year (Oct-Sep, in this case) precipitation and streamflow data gives an elasticity of 1.65, indicating a 10% change in annual precipitation would result in a 16.5% change in annual streamflow. Further analysis shows that the water year data has a much stronger precipitation-streamflow relationship (R 2 =0.72) compared with the calendar year data (R 2 =0.23). From this we infer that the precipitation elasticity of streamflow should be calculated using the seasonality that produces the strongest precipitation-streamflow relationship, as the precipitation elasticity of streamflow is sensitive to the strength of the precipitation-streamflow relationship. A useful practice is to use water year data series - with annual cycles starting from the beginning of the dry season or from just before the snowmelt period. The results of the temporal variations of precipitation elasticity of streamflow for 30 and 50 year blocks indicate that 50-yr-block time series generate less temporal variation, as expected given the longer time period used. These results have implications for the use of elasticity as a rule of thumb under changing climate and climate change and variability impact studies. It can be argued that it is better to use as long a record as possible to estimate elasticity as this captures the range of observed variability. However, with potential climate change and variability, there may be good reason to use only the most recent record (e.g., the most recent 30-50 years) to estimate the elasticity, or use dry or wet periods that are similar to future projections to quantify the climatic change impacts on streamflow. The relationships between the elasticity values and annual rainfall, annual streamflow, and the runoff coefficient (ratio of annual streamflow to annual precipitation) for the Murray-Darling Basin indicate that the elasticity values are generally negatively correlated to these streamflow and runoff coefficient , i.e., a low flow period in general results in a larger elasticity value. Moreover, the relationship between elasticity and the runoff coefficient is stronger than that between elasticity and rainfall, with correlation coefficients of - 0.423 and 0.089 respectively. The relationship between elasticity and streamflow is in-between at -0.260. This implies that annual rainfall is not the only driving force causing larger elasticities in recent years. These research results can be used to guide the application of elasticity methods to quantify hydrological responses to climatic change that inform long-term water management strategies.

Modelling impacts of changes in carbon dioxide concentration, climate and nitrogen deposition on carbon sequestration by European forests and forest soils

The increased risk of flooding due to global warming and subsequent heavy rainfall events in the Nordic-Baltic region, call for recommendations directed at long-term planning. One example of such recommendations are climate change allowances. These are often based on expected changes in design precipitation as given by climate model simulations, and are used as a buffer on top of current design values to avoid a future increased damage potential as a consequence of climate change. We here compute expected changes in precipitation design values, so-called climate factors (CFs), for the Nordic-Baltic region, based on 3 km convection permitting simulations from the Nordic Convection Permitting Climate Projections project. These have the advantage of explicitly resolving convection, which has been shown to be the main contributor to increased rainfall. We assess the dependence of the CFs on rainfall duration, return period, and geographical location, focusing on the summer (convective) season, short durations and the high emission scenario RCP8.5. We also compare these CFs to those computed from a non-convection permitting regional climate model ensemble.We found higher CFs for the longer return period, with only few exceptions, and distinctly higher CFs going from daily to sub-daily durations. However, the different simulations give conflicting results for very short-duration rainfall (<3 h). The huge difference in the climate sensitivity of driving GCMs dominates the magnitude of estimated return levels. Our analysis is shaped by the high computational costs of running convection permitting models, resulting in a very limited ensemble (3 members) representing a single emission scenario (RCP8.5). Therefore, we believe that combining results from convection permitting simulations with a larger ensemble (21 members) of non-convection permitting simulations adds value to the assessment of robust climate change allowances for heavy precipitation in the Nordic-Baltic region.

An assessment of the plausible climate change in precipitation and surface air temperature (SAT) over the European region by the end of the 21st century is provided. The assessment is based on the results of output of the ocean–atmosphere models participating in the Coupled Model Intercomparison Project, phase 5 (CMIP5). Six climate models that best reproduce the historical behaviour of SAT over greater Europe were selected from the CMIP5 project using a performance-based selection method of CMIP5 general circulation models for further assessments. The analysis of historical simulations within the scope of the CMIP5 project reveals that six models (namely, CNRM-CM5, HadGEM2ES, GFDL-CM3, CanESM2, MIROC5 and MPI-ESM-LR) sufficiently reproduce historical tendencies and natural variability over the region of interest. The climate change in SAT and precipitation by the end of the 21st century (2070–2099) was examined within the scope of RCP4.5 and RCP8.5 scenarios for these selected models. Typical regional warming due to RCP4.5 (RCP8.5) scenario is assessed as 3–4.5 °C (as 4–8 °C) in summer and winter, while a significant reduction of precipitation (typically 20–40%) is obtained only in summer.

Changes in both air temperature and precipitation are expected as atmospheric CO2 increases because of anthropogenic activities. These changes will not be evenly distributed across seasons because of the complexity of climate patterns. Using climate, hydrology, and water chemistry data, we investigated how changes in the seasonal climates of the Atlantic Provinces of Canada would change both hydrology and geochemical cycling of 14 river basins influenced by wetlands. We calculated monthly discharge (Q) and dissolved organic carbon (DOC) and dissolved organic nitrogen (DON) exports from 1983 to 1992 and developed three neural network models relating climate variability to hydrologic and geochemical fluxes from the basins. We then identified a series of potential temperature/precipitation scenarios which we applied to the models. We then studied how changes in climate regimes would affect water discharge and aquatic dissolved carbon and nitrogen released from these basins. Our results predict a complex series of possible outcomes for water and elemental fluxes. Warming or cooling in each season combined with potential changes in precipitation will cause completely different outcomes as snowpack melting dominates winter and early spring conditions and evapotranspiration controls summer and fall. A warming winter would cause less snow storage and more runoff, with little evapotranspiration. Warmer summers, on the other hand, will be influenced by evapotranspiration, and water flows will be lower. Carbon and nitrogen exports from basins generally mirror those of Q. Our results suggest that major hydrological changes in basins may lead to significant ecological and water resource impacts, especially in the spring and early summer.

In grassland ecosystems, where soil water most frequently controls ecosystem processes, expected changes in precipitation and temperature may have dramatic effects on ecosystem dynamics. Previous observational studies have reported that aboveground net primary production (ANPP) in grasslands is very sensitive to changes in precipitation. Yet, we lack experimentally based evidence to support these observations. Further, most of the studies have focused solely on ANPP, neglecting belowground production (BNPP). This is an important gap in our knowledge, as BNPP represents 50% or more of total net primary production (NPP) in grasslands. Here, we present results from a 3-year water manipulation experiment (2008–2010) at two sites in the central grassland region of North America, USA. We were successful in changing the soil water content in our treatments, but these changes resulted in different, but significant responses in ANPP and BNPP at our two sites. At the shortgrass steppe, we found that neither NPP nor ANPP were sensitive to treatment precipitation, and although we found BNPP was sensitive to changes in treatment precipitation, the direction of the response varied between years. In contrast, ANPP was very sensitive to treatment precipitation on the mixed-grass prairie, whereas BNPP was insensitive. Based on our finding that two grassland ecosystems showed dramatically different above and belowground production responses to soil water manipulations, we cannot assume that predicted changes in climate will cause similar above- and belowground production responses. Second, our results demonstrated that sites within the same region may differ markedly in the sensitivity of ANPP to changes in growing season precipitation.

Changes in major climatic and hydrological quantities in the upper Mississippi River basin and their interrelationships are studied with the Soil and Water Assessment Tool being driven by the contemporary climate and future scenario simulations of 10 global models in the Intergovernmental Panel on Climate Change (IPCC) Data Archive. Although the seasonal cycles of climate and hydrological quantities simulated by the 10 models have differences, the ensemble is very close to the observation. Ensemble predictions show that with warming in all months, precipitation decreases in summer but increases in all other seasons. Correspondingly, streamflow decreases in all seasons except winter, evapotranspiration decreases in July–September and increases in all other months, and snowmelt increases in winter but decreases in spring and fall. To understand the linkages between the cross-century changes of climate and hydrological quantities and the relative importance of the changes of temperature and precipitation to the changes of hydrological quantities, relationships between interannual variations of these quantities are investigated. It is shown that the change rates of the hydrological quantities with respect to temperature and precipitation obtained from regressions of interannual variations can vary greatly from month to month; however, on a monthly basis, they do not change much from the current to the future periods. Evaluations with these change rates indicate that for interannual variations of hydrological quantities, both variations of temperature and precipitation are important, and their relative importance depends on the month of the year. However, the changes of hydrological quantities from the means of the current years to the means of the future are dominated by warming in all months, and the influence from change of precipitation is much smaller. The changes of the hydrological quantities can be well predicted with the change rates from the warming alone.

Reconstruction of seasonal precipitation in Hawai'i using high-resolution carbon isotope measurements across tree rings

Highest telescope

The water demand in arid regions is commonly covered by groundwater resources that date back to more humid periods of the Pleistocene and Holocene. Within the investigated arid part of SE Saudi-Arabia information about climate, groundwater levels, and pumping rates are only available for regions where groundwater extractions occur at present-day. For the prediction of the impact of long-term climate changes on groundwater resources an understanding of the hydrogeological and hydrological past and the development of the aquifers is necessary. Therefore, all available information about hydrology and hydrogeology for the past 10,000 years BP were collected and compiled to a conceptual model of the aquifer development on the Arabian Peninsula since the last Ice-Age. The climatic history was displayed by changes in precipitation, temperature and recharge during the mid-S and late Holocene. The hydrogeological development is described by groundwater ages, sea level fluctuations, movement of the coastline, and the development of sabkhas. The most sensitive parameter to describe the development of aquifer system is recharge. Present-day recharge was calculated with the hydrological model system HEC-HMS accounting for current precipitation, temperature, wind, soil types, and geomorphology. With respect to changes in precipitation and temperature over the past 10,000 years the temporal and spatial variability of groundwater recharge was calculated using empirical equations valid for semi-arid and arid settings. Further inflow into the groundwater system results from surface water infiltration in wadi beds, while natural outflow from the groundwater system occurs by discharge to the Gulf, evaporation from sabkhas, and spring discharge. Backward predictions can be verified by sedimentological observations of palaeo-river systems and lakes indicating that groundwater levels reached temporarily the surface under wetter climate conditions and 14C groundwater ages displaying groundwater residence times.

Riders under storms: Contributions of nomadic herders’ observations to analysing climate change in Mongolia

This article discusses modelling of Aboveground Net Primary Production (ANPP) of steppe (arid grassland ecosystems) plant species in relation to changes in total precipitation over the previous year at the “Stara” study site, Biosphere Reserve “Askania-Nova”, Khersonregion (Ukraine). To investigate linkages between precipitation and Aboveground Net Primary Production, correlation analysis was chosen and a time series regression analysis was based on the data set for the period 1988–2012. The NPP dependence on quantity of precipitation was found to be more significant for the previous autumn-winter-spring period (AWSP) than for the previous 12 month period. A regression model of ANPP’s dependence on AWSP is proposed. This model was further validated by the authors’ samples of ANPP, collected at the “Stara” study site in 2013–2016. The regression model showed a non-linear (quadratic) dependence of net primary production of zonal and intrazonal plant coenoses and total precipitation for the autumn-winter-spring period for arid grasslands with a coefficient of determination equal to 0.54 and significance level less than 0.05. The non-linear equation for these relations, visualized by a parabola curve, was calculated using the Nonlinear Least-Squares Regression Method. The data set, based on calculated predicted values, using the calculated equation, had a similar dynamic to the historical data on ANPP, but the model could not predict critical values. For this reason, additional studies are required for critical precipitation events. Non-linear response, investigated according to regression analysis, reveals optimal zones of plant growth, depending on the total precipitation level before the vegetation peak. For research areas where the dominant species are the turf grasses Stipa ucrainica P. Smirn., S. capillata L., S. lessingiana Trin. & Rupr., Festuca valesiaca Gaudin, Koeleria cristata (L.) Pers.) the optimal precipitation rates were found to be 350–400 mm during AWSP with ANPP at 350 g/m 2 . On the basis of the regression model and current forecasts of changes in precipitation rates we made a forecast of net primary production of plant communities for four climate change scenarios (RCP2.6, RCP4.5, RCP6, and RCP8.5) described in the Fifth Assessment of Intergovernmental Panel on Climate Change (IPCC). For this purpose, bioclimate projections of 10 major climate models (The Community Climate System Model Version 4 (CCSM4), GISS-E2-R, HadGEM2-AO, HadGEM2-ES, IPSL-CM5A-LR, MIROC-ESM-CHEM, MIROC-ESM, MIROC5, MRI-CGCM3, NorESM1-M), used for preparation of the IPCC report, were analyzed and imported to the geographical information system package QGIS. QGIS modelling software was used for geoanalysis and calculation of GIS-layers for Askania-Nova and adjacent arid grasslands. The results of modelling with the 10 climate models were compared and analyzed for each of the four IPCC scenarios, depending on predicted CO 2 levels. The presented modelling results showed a trend to growth in AWSP precipitation and NPP for all scenarios up to 2040–2060. The scenarios RCP2.6, RCP4.5, RCP6 predicted the optimum precipitation zone for current plant diversity for the period of 2040–2060 and scenario RCP8.5 predicted an optimum zone peak after 2080. The study confirmed the importance of monitoring the productivity of herbaceous communities in dry steppe ecosystems ofUkraine.

Timing of past glaciation at the Sierra de Aconquija, northwestern Argentina, and throughout the Central Andes

Estimates of the projected changes in precipitation and temperature have great significance for adaption planning in the context of climate change. To obtain the climate change information at regional or local scale, downscaling approaches are required to downscale the coarse global climate model (GCM) outputs to finer resolutions in both spatial and temporal dimensions. The multi-site, multi-variate downscaling approach has received considerable attention recently due to its advantage in providing distributed, physically coherent downscaled meteorological fields for subsequent impact modeling. In this study, a newly developed multi-site multivariate statistical downscaling approach based on empirical copula was applied to downscale grid-based, monthly precipitation, maximum and minimum temperature outputs from nine global climate models to site-specific, daily data over four weather stations in Singapore. The advantage of this approach lies in its ability to reflect the at-site statistics, inter-site and inter-variable dependencies, and temporal structure in the downscaled data. The downscaling was conducted for two projection periods (i.e., the 2021–2050 and 2071–2100 periods) under two emission scenarios (i.e., representative concentration pathway (RCP)4.5 and RCP8.5 scenarios). Based on the downscaling results, projected changes in daily precipitation, maximum and minimum temperatures were examined. The results show that there is no consensus on the projected change in average precipitation over the two future periods. The major uncertainty for precipitation projection comes from the GCMs. For daily maximum and minimum temperatures, all downscaled GCMs project an increase of average temperature in the future. These change signals could be different from those of the original GCM data, both in magnitude and in direction. These findings could assist in adaption planning in Singapore in response to emerging climate risks.