Observed and projected changes in extreme drought and wet-prone regions over India under CMIP5 RCP8.5 using a new vulnerability index

Past versions of vulnerability index have shown ability to detect susceptible region by assessing socio-economic parameters at local scales. However, due to variability of these vulnerability index respect to socio-economic parameters, cann’t be utilized to predict the susceptibility region. The present endeavor aims to develops a new vulnerable index which identify and predict the spatio-temporal imprint of extreme drought and wet events at various scales 1o×1o in India by analyzing monthly observed and Coupled Model Inter-Comparison Phase 5 (CMIP5) rainfall data at spatial scale of time period pertaining to 1901-2100. New vulnerability index is proposed by consolidating the outcomes of Standard Precipitation Index (SPI) at different time scales such as 3- and 12-month and along with weights of individual grids. The weights of individual grid is calculated through the occurrence of extreme drought and wet events in the recent past which is to include a climate change factor in the proposed index. Based on the spatial distribution of high index values, the expected vulnerable regions concerning extreme drought events will be in Northeast, Northeast Central, East Coast, West, Northwest, Northcentral, and some grids in South part of India. Similarly, vulnerable regions concerning extreme wet events are likely to be in the Northeast, West Coast, East Coast, and some grids in the Peninsular region.Further, a conceptual model is presented to quantify the severity of extreme events. The analyses reveal that on the CMIP5 model data, it is obtained that 2024, 2026-27, 2035, 2036-37, 2043-44, 2059-60, 2094 are likely to be the most prominent drought years in all-India monsoon rainfall and their impact will persist for a longer time. Similarly, the most prominent wet events are predicted to be 2076, 2079-80, 2085, 2090, 2092, and 2099.

Temperature Anomalies, Long Memory, and Aggregation

Econometric studies for global heating have typically used regional or global temperature averages to study its long memory properties. One typical explanation behind the long memory properties of temperature averages is cross-sectional aggregation. Nonetheless, formal analysis regarding the effect that aggregation has on the long memory dynamics of temperature data has been missing. Thus, this paper studies the long memory properties of individual grid temperatures and compares them against the long memory dynamics of global and regional averages. Our results show that the long memory parameters in individual grid observations are smaller than those from regional averages. Global and regional long memory estimates are greatly affected by temperature measurements at the Tropics, where the data is less reliable. Thus, this paper supports the notion that aggregation may be exacerbating the long memory estimated in regional and global temperature data. The results are robust to the bandwidth parameter, limit for station radius of influence, and sampling frequency.

Predictive Skill Assessment for Land Water Storage in CMIP5 Decadal Hindcasts by a Global Reconstruction of GRACE Satellite Data

The evaluation of decadal climate predictions against observations is crucial for their benefit to stakeholders. While the skill of such forecasts has been verified for several atmospheric variables, land hydrological states such as terrestrial water storage (TWS) have not been extensively investigated yet due to a lack of long observational records. Anomalies of TWS are globally observed with the satellite missions GRACE (2002–2017) and GRACE-FO (since 2018). By means of a GRACE-like reconstruction of TWS available over 41 years, we demonstrate that this data type can be used to evaluate the skill of decadal prediction experiments made available from different Earth system models as part of both CMIP5 and CMIP6. Analysis of correlation and root-mean-square deviation (RMSD) reveals that for the global land average the initialized simulations outperform the historical experiments in the first three forecast years. This predominance originates mainly from equatorial regions where we assume a longer influence of initialization due to longer soil memory times. Evaluated for individual grid cells, the initialization has a largely positive effect on the forecast year 1 TWS states; however, a general grid-scale prediction skill for TWS of more than 2 years could not be identified in this study for CMIP5. First results from decadal hindcasts of three CMIP6 models indicate a predictive skill comparable to CMIP5 for the multimodel mean in general, and a distinct positive influence of the improved soil–hydrology scheme implemented in the MPI-ESM for CMIP6 in particular.

Modelling the basal hydrology under the Greenland Ice Sheet

<p>Subglacial hydrology plays an important role in the evolution of ice dynamics. Primarily, it affects basal processes such as basal sliding. Further, subglacial water exiting a calving front incites submarine melt, increasing calving, resulting in a thinning of the interior ice sheet. Knowledge of it is therefore crucial towards the development and improvement of ice sheet models. We implement a model representing the routing of subglacial water below the Greenland ice sheet in either a one, four or eight directional manner. Due to its computational efficiency, the model is suited for coupling with continental scale ice sheet models on very high resolutions (e.g. 150 m).</p><p>Routing depends on the hydraulic potential of individual grid cells which is therefore heavily dependent on accurate estimates of the ice thickness as well as the grid utilized. Sensitivity analyses brought to life that the routing exhibits artefacts resulting in significant flow diversions on high resolutions if the gradients are only considered over the distance of a single grid cell, this is overcome by incorporating a smoothing procedure.</p><p>With the basal water model in place and input of the basal melt rate from the VUB Greenland Ice Sheet Model (GISM) as well as runoff input from the Modèle Athmospherique Régional (MAR), we calculate the inflow of freshwater to several reference fjords for the last thirty years and investigate its temporal and spatial patterns. Jakobshavn Isbrae experiences by far the most freshwater inflow compared to the other reference fjords. Despite limited runoff in the northeast of Greenland, high basal melt rates and a significant catchment area provide the outlets of the Northeast Greenland Ice Stream (NEGIS) with substantial inflow too.</p>

Assessing the performance of the BARCAST climate field reconstruction technique for a climate with long-range memory

The skill of the state-of-the-art climate field reconstruction technique BARCAST (Bayesian Algorithm for Reconstructing Climate Anomalies in Space and Time) to reconstruct temperature with pronounced long-range memory (LRM) characteristics is tested. A novel technique for generating fields of target data has been developed and is used to provide ensembles of LRM stochastic processes with a prescribed spatial covariance structure. Based on different parameter setups, hypothesis testing in the spectral domain is used to investigate if the field and spatial mean reconstructions are consistent with either the fractional Gaussian noise (fGn) process null hypothesis used for generating the target data, or the autoregressive model of order 1 (AR(1)) process null hypothesis which is the assumed temporal evolution model for the reconstruction technique. The study reveals that the resulting field and spatial mean reconstructions are consistent with the fGn process hypothesis for some of the tested parameter configurations, while others are in better agreement with the AR(1) model. There are local differences in reconstruction skill and reconstructed scaling characteristics between individual grid cells, and the agreement with the fGn model is generally better for the spatial mean reconstruction than at individual locations. Our results demonstrate that the use of target data with a different spatiotemporal covariance structure than the BARCAST model assumption can lead to a potentially biased climate field reconstruction (CFR) and associated confidence intervals.

A Postprocessing Method for Seasonal Forecasts Using Temporally and Spatially Smoothed Statistics

A statistical postprocessing method for seasonal forecasts based on temporally and spatially smoothed climate statistics is introduced. The method uses information available from seasonal hindcasts initialized at the beginning of 12 calendar months. The performance of the method is tested within both deterministic and probabilistic frameworks using output from the ensemble of seasonal hindcasts produced by the Canadian Seasonal to Interannual Prediction System for the 30-yr period 1981–2010. Forecast skill improvements are found to be greater when forecast adjustment parameters estimated for individual seasons and at individual grid points are temporally and spatially smoothed. The greatest skill improvements are typically achieved for seasonally invariant parameters while skill improvements due to additional spatial smoothing are modest.

Grid cells show field-to-field variability and this explains the aperiodic response of inhibitory interneurons

Research on network mechanisms and coding properties of grid cells assume that the firing rate of a grid cell in each of its fields is the same. Furthermore, proposed network models predict spatial regularities in the firing of inhibitory interneurons that are inconsistent with experimental data. In this paper, by analyzing the response of grid cells recorded from rats during free navigation, we first show that there are strong variations in the mean firing rate of the fields of individual grid cells and thus show that the data is inconsistent with the theoretical models that predict similar peak magnitudes. We then build a two population excitatory-inhibitory network model in which sparse spatially selective input to the excitatory cells, presumed to arise from e.g. salient external stimuli, hippocampus or a combination of both, leads to the variability in the firing field amplitudes of grid cells. We show that, when combined with appropriate connectivity between the excitatory and inhibitory neurons, the variability in the firing field amplitudes of grid cells results in inhibitory neurons that do not exhibit regular spatial firing, consistent with experimental data. Finally, we show that even if the spatial positions of the fields are maintained, variations in the firing rates of the fields of grid cells are enough to cause remapping of hippocampal cells.

Multi-satellite sensor study on precipitation-induced emission pulses of NO_<i>x</i> from soils in semi-arid ecosystems

We present a top-down approach to infer and quantify rain-induced emission pulses of NOx (≡ NO + NO2), stemming from biotic emissions of NO from soils, globally with a spatial resolution of 0.25° from satellite-borne measurements of NO2. This is achieved by synchronizing time series at single grid pixels according to the first day of rain after a dry spell of prescribed duration. The full track of the temporal evolution several weeks before and after a rain pulse is retained with daily resolution. These are needed for a sophisticated background correction, which accounts for seasonal variations in the time series and allows for improved quantification of rain-induced soil emissions. We find strong peaks of enhanced NO2 Vertical Column Densities (VCDs) on the first day of rainfall after prolonged droughts in many semi-arid regions of the world, in particular in the Sahel. Detailed investigations show that the rain-induced NO2 pulse detected by the OMI, GOME-2 and SCIAMACHY satellite instruments could not be explained by other sources, such as biomass burning or lightning, or by retrieval artefacts (e.g. due to clouds). For the Sahel region, absolute enhancements of the NO2 VCDs on the first day of rain based on OMI measurements 2007–2010 are on average 4 × 1014 molec cm−2 and exceed 1 × 1015 molec cm−2 for individual grid cells. Assuming a NOx lifetime of 4 h, this corresponds to soil NOx emissions in the range of 6 ng N m−2 s−1 up to 65 ng N m−2 s−1, in good agreement with literature values. Apart from the clear first-day peak, NO2 VCDs show moderately enhanced NO2 VCDs of 2 × 1014 molec cm−2 compared to background over the following two weeks suggesting potential further emissions during that period of about 3.3 ng N m−2 s−1.

Investigating uncertainties in global gridded datasets of climate extremes

We assess the effects of different methodological choices made during the construction of gridded datasets of climate extremes, focusing primarily on HadEX2. Using global timeseries of the indices and their coverage, as well as uncertainty maps, we show that the choices which have the greatest effect are those relating to the station network used or which drastically change the values for individual grid boxes. The latter are most affected by the number of stations required in or around a grid box and the gridding method used. Most parametric changes have a small impact, on global and on grid box scales, whereas structural changes to the methods or input station networks may have large effects. On grid box scales, trends in temperature indices are very robust to most choices, especially in areas which have high station density (e.g. North America, Europe and Asia). Precipitation trends, being less spatially coherent, can be more susceptible to methodological changes, but are still clear in regions of high station density. Regional trends from all indices derived from areas with few stations should be treated with care. On a global scale, the linear trends over 1951–2010 from almost all choices fall within the statistical range of trends from HadEX2. This demonstrates the robust nature of HadEX2 and related datasets to choices in the creation method.