Thermodynamics of the evolution of the biosphere

The thermodynamic foundations of the evolution of the biosphere are considered: the variability of natural systems with different dispersion of their components, the alternative ways of development of such systems, the alternative of intermediate stable states of ecosystems depending on fluctuations of external factors, primarily climate. The necessity of developing a system of mutually agreed complex indicators of this process is postulated. The necessity of including the water content of ecosystems in the number of parameters of nonequilibrium thermodynamics is justified. A new section of land hydrology is formulated -the study of thermodynamic aspects of the dynamics of natural waters.

Data assimilation of GRACE terrestrial water storage to improve sea level rise estimates and isolate anthropogenic influences

<p>Sea level rise (SLR) projections rely on the accurate and precise closure of Earth’s water budget. The Gravity Recovery and Climate Experiment (GRACE) mission has provided global-coverage observations of terrestrial water storage (TWS) anomalies that improve accounting of ice and land hydrology changes and how these changes contribute to sea level rise. The contribution of land hydrology TWS changes to sea level rise is much smaller and less certain than contributions from glacial melt and thermal expansion. Although land hydrology TWS plays a smaller role, it is still important to investigate to improve the precision of the overall global water budget. This study analyzes how data assimilation techniques improve estimates of the land hydrology contribution to sea level rise. To achieve this, three global TWS datasets were analyzed: (1) GRACE TWS observations alone, (2) TWS estimates from the model-only simulation using Catchment Land Surface Model, and (3) TWS estimates from a data assimilation product of (1) and (2). We compared the data assimilation product with the GRACE observations alone and the model-only simulation to isolate the contribution to sea level rise from anthropogenic activities. We assumed a balanced water budget between land hydrology and the ocean, thus changes in global TWS are considered equal and opposite to sea level rise contribution.  Over the period of 2003-2016, we found sea level rise contributions from each dataset of +0.35 mm SLR eq/yr for GRACE, -0.34 mm SLR eq/yr for model-only, and a +0.09 mm SLR eq/yr for DA (reported as the mean linear trend). Our results indicate that the model-only simulation is not capturing important hydrologic processes. These are likely anthropogenic driven, indicating direct anthropogenic and climate-driven TWS changes play a substantial role in TWS contribution to SLR.</p>

A Conceptual Framework for Modelling the Climate Change and its Impacts within a River Basin using Remote Sensing data

<p><strong>Abstract</strong></p> <p>In general, modelling the climate change and its impacts within a hydrological unit brings out an understanding of the system and, its behaviour with various model constrains. The climate change and global warming studies are being under research and development phase, because of its complex and dynamic nature. The IPCC 5<sup>th</sup> Assessment Report on global warming states that in the 21<sup>st</sup> century, there may be an increase in temperature of the order of ~1.5°C. This transient climate may cause significant impacts or any discrepancies in the water availability of the hydrological unit. This may lead to severe impacts in countries with high population such as India, China, etc., The Remote sensing datasets play an essential role in modelling the climatic changes for a river basin at different spatial and temporal scales. This study aims to propose a conceptual framework for the above-defined problem with emphasising on remote sensing datasets. This framework involves five entities such as the data component, process component,  impact component,  feedback component and, uncertainty component. The framework flow begins with the data component entity that involves two significant inputs, such as the hydro-meteorological data and the land-hydrology data. The essential attributes of the hydro-meteorological data entities are the precipitation, temperature, relative humidity, wind speed and solar radiation. These datasets may be obtained and analysed from empirical or statistical methods, in-situ based or satellite-based methods, respectively. These mathematical models on long-run historical climate data may provide knowledge on climate change detections or its trends. The meteorological data derived from the satellites may have a measurable bias with that of the in situ data. The satellite-based land-hydrology data component involves various attributes such as topography, soil, vegetation, water bodies, other land use / land cover, soil moisture, evapotranspiration. The process component involves complex land-hydrology processes that may be well established and modelled by customizable hydrological models. Here, we may emphasise the use of remote-sensing based model parameter values in the equations either directly or indirectly. Also, the land-atmospheric process component involves various complex processes that may take place in this zone. These processes may be well established and solved by customizable atmospheric weather models. The land components play a significant role in modelling the climate changes, because these land processes may trigger global warming by various anthropogenic agents. The main objective of this framework is to emphasise the climate change impacts using remote sensing. Hence, the impact component entity plays an essential role in this conceptual framework. The climate change impact within a river basin at various spatial and temporal scales are identified using different hydrological responses. The feedback entity is the most sensitive part of this framework, because it may alter the climate forcing either positive or negative. An uncertainty model component handles the uncertainty in the model framework. The highlight of this conceptual framework is to use the remote sensing datasets in climate change studies. The limitations on the correctness of the remote sensing data with the insitu data at every location is not feasible.</p>

Causality and Evolution of Summer Polynyas off the Coast of Northern Greenland

<p>The summer polynya along the northern coast of Greenland has been observed only six months later after the winter polynya in 2018, which has prompted concerns about the stability of some of the thickest sea-ice in the Arctic region. This study combines retrospective remotely sensed sea-ice measurements with results from the Regional Arctic System Model (RASM) to examine the causes, effect, and evolution of open-water areas/polynyas in the region.</p><p>RASM is a limited-domain, fully-coupled climate model, consisting of the atmosphere (Weather Research and Forecasting, WRF3.7), ocean (Los Alamos National Laboratory Parallel Ocean Program, POP2), sea-ice (Community Sea Ice Model, CICE5), land hydrology (Variable Infiltration Capacity, VIC4) and streamflow routing (RVIC) components. The ocean and sea-ice models are configured with the horizontal resolution of 1/12-degree with 45 vertical levels and 5 sea-ice thickness categories, respectively. The atmosphere and land hydrology components are set up on a 50-km grid with 40-vertical levels and 3-soil layers, respectively. The Climate Forecast System Reanalysis (CFSR) and version 2 (CFSv2) output are used as boundary conditions for dynamic downscaling.</p><p>Analysis of the sea-ice conditions off the coast of northern Greenland revealed that RASM, in agreement with satellite measurements, has simulated five summer polynya events, i.e. in August of 1984, 1985, 2002, 2004 and 2018, over the 39-year period (1980-2018). All these events were primarily dynamically forced, with the thermodynamic forcing playing the secondary, yet still important role. While the thermodynamically driven sea-ice melting exhibited a relatively little year-to-year variability, between 87 km<sup>3</sup> and 115 km<sup>3</sup>, its relative contribution to the total sea-ice loss increased by 2.5 times, from 16% in 1984 to 40% in 2018. This implies that with continuing thinning of sea-ice, increasingly less mechanical forcing may be required to generate and maintain a polynya or open water north of Greenland in summers to come.  </p>