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The CryoGrid community model is a flexible toolbox for simulating the ground thermal regime and the ice-water balance for permafrost and glaciers, extending a well-established suite of permafrost models (CryoGrid 1, 2, and 3). The CryoGrid community model can accommodate a wide variety of application scenarios, which is achieved by fully modular structures through object-oriented programming. Different model components, characterized by their process representations and parameterizations, are realized as classes (i.e., objects) in CryoGrid. Standardized communication protocols between these classes ensure that they can be stacked vertically. For example, the CryoGrid community model features several classes with different complexity for the seasonal snow cover, which can be flexibly combined with a range of classes representing subsurface materials, each with their own set of process representations (e.g., soil with and without water balance, glacier ice). We present the CryoGrid architecture as well as the model physics and defining equations for the different model classes, focusing on one-dimensional model configurations which can also interact with external heat and water reservoirs. We illustrate the wide variety of simulation capabilities for a site on Svalbard, with point-scale permafrost simulations using, e.g., different soil freezing characteristics, drainage regimes, and snow representations, as well as simulations for glacier mass balance and a shallow water body. The CryoGrid community model is not intended as a static model framework but aims to provide developers with a flexible platform for efficient model development. In this study, we document both basic and advanced model functionalities to provide a baseline for the future development of novel cryosphere models.

The CryoGrid community model is a flexible toolbox for simulating the ground thermal regime and the ice-water balance for permafrost and glaciers, extending a well-established suite of permafrost models (CryoGrid 1, 2, and 3). The CryoGrid community model can accommodate a wide variety of application scenarios, which is achieved by fully modular structures through object-oriented programming. Different model components, characterized by their process representations and parameterizations, are realized as classes (i.e., objects) in CryoGrid. Standardized communication protocols between these classes ensure that they can be stacked vertically. For example, the CryoGrid community model features several classes with different complexity for the seasonal snow cover, which can be flexibly combined with a range of classes representing subsurface materials, each with their own set of process representations (e.g., soil with and without water balance, glacier ice). We present the CryoGrid architecture as well as the model physics and defining equations for the different model classes, focusing on one-dimensional model configurations which can also interact with external heat and water reservoirs. We illustrate the wide variety of simulation capabilities for a site on Svalbard, with point-scale permafrost simulations using, e.g., different soil freezing characteristics, drainage regimes, and snow representations, as well as simulations for glacier mass balance and a shallow water body. The CryoGrid community model is not intended as a static model framework but aims to provide developers with a flexible platform for efficient model development. In this study, we document both basic and advanced model functionalities to provide a baseline for the future development of novel cryosphere models.

Northern peatlands store 300-600 Pg C, of which approximately half are underlain by permafrost. Climate warming and, in some regions, soil drying from enhanced evaporation are progressively threatening this large carbon stock. Here, we assess future CO2 and CH4 fluxes from northern peatlands using five land surface models that explicitly include representation of peatland processes. Under Representative Concentration Pathways (RCP) 2.6, northern peatlands are projected to remain a net sink of CO2 and climate neutral for the next three centuries. A shift to a net CO2 source and a substantial increase in CH4 emissions are projected under RCP8.5, which could exacerbate global warming by 0.21 degrees C (range, 0.09-0.49 degrees C) by the year 2300. The true warming impact of peatlands might be higher owing to processes not simulated by the models and direct anthropogenic disturbance. Our study highlights the importance of understanding how future warming might trigger high carbon losses from northern peatlands.

Northern peatlands store 300–600 Pg C, of which approximately half are underlain by permafrost. Climate warming and, in some regions, soil drying from enhanced evaporation are progressively threatening this large carbon stock. Here, we assess future CO2 and CH4 fluxes from northern peatlands using five land surface models that explicitly include representation of peatland processes. Under Representative Concentration Pathways (RCP) 2.6, northern peatlands are projected to remain a net sink of CO2 and climate neutral for the next three centuries. A shift to a net CO2 source and a substantial increase in CH4 emissions are projected under RCP8.5, which could exacerbate global warming by 0.21°C (range, 0.09–0.49°C) by the year 2300. The true warming impact of peatlands might be higher owing to processes not simulated by the models and direct anthropogenic disturbance. Our study highlights the importance of understanding how future warming might trigger high carbon losses from northern peatlands.

Peatlands, with high spatial variability in ecotypes and microforms, constitute a significant part of the boreal landscape and play an important role in the global carbon (C) cycle. However, the effects of this peatland heterogeneity within the boreal landscape are rarely quantified. Here, we use field-based measurements, high-resolution land cover classification, and biogeochemical and atmospheric models to estimate the atmosphere-ecosystem C fluxes and corresponding radiative effect (RE) for a boreal landscape (Kaamanen) in northern Finland. Our result shows that the Kaamanen catchment currently functioned as a sink of carbon dioxide (CO2) and a source of methane (CH4). Peatlands (26% of the area) contributed 22% of the total CO2 uptake and 89% of CH4 emissions; forests (61%) accounted for 78% of CO2 uptake and offset 6% of CH4 emissions; water bodies (13%) offset 7% of CO2 uptake and contributed 11% of CH4 emissions. The heterogeneity of peatlands accounted for 11%, 88%, and 75% of the area-weighted variability (deviation from the area-weighted mean among different land cover types (LCTs) within the catchment) in CO2 flux, CH4 flux, and the combined RE of CO2 and CH4 exchanges over the 25-yr time horizon, respectively. Aggregating peatland LCTs or misclassifying them as non-peatland LCTs can significantly (p < 0.05) bias the regional CH4 exchange and RE estimates, while differentiating between drier non-inundated and wetter inundated peatlands can effectively reduce the bias. Current land cover products lack such details in peatland heterogeneity, which would be needed to better constrain boreal C budgets and global C-climate feedbacks.

Northern peatlands store 300–600 Pg C, of which approximately half are underlain by permafrost. Climate warming and, in some regions, soil drying from enhanced evaporation are progressively threatening this large carbon stock. Here, we assess future CO2 and CH4 fluxes from northern peatlands using five land surface models that explicitly include representation of peatland processes. Under Representative Concentration Pathways (RCP) 2.6, northern peatlands are projected to remain a net sink of CO2 and climate neutral for the next three centuries. A shift to a net CO2 source and a substantial increase in CH4 emissions are projected under RCP8.5, which could exacerbate global warming by 0.21°C (range, 0.09–0.49°C) by the year 2300. The true warming impact of peatlands might be higher owing to processes not simulated by the models and direct anthropogenic disturbance. Our study highlights the importance of understanding how future warming might trigger high carbon losses from northern peatlands.

Northern peatlands store 300–600 Pg C, of which approximately half are underlain by permafrost. Climate warming and, in some regions, soil drying from enhanced evaporation are progressively threatening this large carbon stock. Here, we assess future CO2 and CH4 fluxes from northern peatlands using five land surface models that explicitly include representation of peatland processes. Under Representative Concentration Pathways (RCP) 2.6, northern peatlands are projected to remain a net sink of CO2 and climate neutral for the next three centuries. A shift to a net CO2 source and a substantial increase in CH4 emissions are projected under RCP8.5, which could exacerbate global warming by 0.21°C (range, 0.09–0.49°C) by the year 2300. The true warming impact of peatlands might be higher owing to processes not simulated by the models and direct anthropogenic disturbance. Our study highlights the importance of understanding how future warming might trigger high carbon losses from northern peatlands.

Northern peatlands store 300–600 Pg C, of which approximately half are underlain by permafrost. Climate warming and, in some regions, soil drying from enhanced evaporation are progressively threatening this large carbon stock. Here, we assess future CO2 and CH4 fluxes from northern peatlands using five land surface models that explicitly include representation of peatland processes. Under Representative Concentration Pathways (RCP) 2.6, northern peatlands are projected to remain a net sink of CO2 and climate neutral for the next three centuries. A shift to a net CO2 source and a substantial increase in CH4 emissions are projected under RCP8.5, which could exacerbate global warming by 0.21°C (range, 0.09–0.49°C) by the year 2300. The true warming impact of peatlands might be higher owing to processes not simulated by the models and direct anthropogenic disturbance. Our study highlights the importance of understanding how future warming might trigger high carbon losses from northern peatlands.

Northern peatlands store 300–600 Pg C, of which approximately half are underlain by permafrost. Climate warming and, in some regions, soil drying from enhanced evaporation are progressively threatening this large carbon stock. Here, we assess future CO2 and CH4 fluxes from northern peatlands using five land surface models that explicitly include representation of peatland processes. Under Representative Concentration Pathways (RCP) 2.6, northern peatlands are projected to remain a net sink of CO2 and climate neutral for the next three centuries. A shift to a net CO2 source and a substantial increase in CH4 emissions are projected under RCP8.5, which could exacerbate global warming by 0.21°C (range, 0.09–0.49°C) by the year 2300. The true warming impact of peatlands might be higher owing to processes not simulated by the models and direct anthropogenic disturbance. Our study highlights the importance of understanding how future warming might trigger high carbon losses from northern peatlands.

The majority of northern peatlands were initiated during the Holocene. Owing to their mass imbalance, they have sequestered huge amounts of carbon in terrestrial ecosystems. Although recent syntheses have filled some knowledge gaps, the extent and remoteness of many peatlands pose challenges to developing reliable regional carbon accumulation estimates from observations. In this work, we employed an individual- and patch-based dynamic global vegetation model (LPJ-GUESS) with peatland and permafrost functionality to quantify long-term carbon accumulation rates in northern peatlands and to assess the effects of historical and projected future climate change on peatland carbon balance. We combined published datasets of peat basal age to form an up-to-date peat inception surface for the pan-Arctic region which we then used to constrain the model. We divided our analysis into two parts, with a focus both on the carbon accumulation changes detected within the observed peatland boundary and at pan-Arctic scale under two contrasting warming scenarios (representative concentration pathway-RCP8.5 and RCP2.6). We found that peatlands continue to act as carbon sinks under both warming scenarios, but their sink capacity will be substantially reduced under the high-warming (RCP8.5) scenario after 2050. Areas where peat production was initially hampered by permafrost and low productivity were found to accumulate more carbon because of the initial warming and moisture-rich environment due to permafrost thaw, higher precipitation and elevated CO2 levels. On the other hand, we project that areas which will experience reduced precipitation rates and those without permafrost will lose more carbon in the near future, particularly peatlands located in the European region and between 45 and 55 degrees N latitude. Overall, we found that rapid global warming could reduce the carbon sink capacity of the northern peatlands in the coming decades.

Curcumin is the natural herb that shows effect in the treatment of cancer and rheumatoid arthritis and methotrexate is well known anticancer and anti-rheumatoid drug. Literature survey reveals that there was no method available for the selected drug combination. So, here an attempt has been made to develop simple, rapid and economic method for simultaneous estimation of methotrexate and curcumin in bulk drug by using RP-HPLC method. The percentage assay from optimized method was found to be 99.68% and 99.76% for methotrexate and curcumin respectively.  The proposed method was validated for  linearity, accuracy, precision and robustness according to ICH guidelines and were found to be   within the standard range. Keywords: RP- HPLC, methotrexate, Linearity, anticancer

Curcumin is the natural herb that shows effect in the treatment of cancer and rheumatoid arthritis and methotrexate is well known anticancer and anti-rheumatoid drug. Literature survey reveals that there was no method available for the selected drug combination. So, here an attempt has been made to develop simple, rapid and economic method for simultaneous estimation of methotrexate and curcumin in bulk drug by using RP-HPLC method. The percentage assay from optimized method was found to be 99.68% and 99.76% for methotrexate and curcumin respectively.  The proposed method was validated for  linearity, accuracy, precision and robustness according to ICH guidelines and were found to be   within the standard range. Keywords: RP- HPLC, methotrexate, Linearity, anticancer

Effective societal responses to rapid climate change in the Arctic rely on an accurate representation of region-specific ecosystem properties and processes. However, this is limited by the scarcity and patchy distribution of field measurements. Here, we use a comprehensive, geo-referenced database of primary field measurements in 1,840 published studies across the Arctic to identify statistically significant spatial biases in field sampling and study citation across this globally important region. We find that 31% of all study citations are derived from sites located within 50 km of just two research sites: Toolik Lake in the USA and Abisko in Sweden. Furthermore, relatively colder, more rapidly warming and sparsely vegetated sites are under-sampled and under-recognized in terms of citations, particularly among microbiology-related studies. The poorly sampled and cited areas, mainly in the Canadian high-Arctic archipelago and the Arctic coastline of Russia, constitute a large fraction of the Arctic ice-free land area. Our results suggest that the current pattern of sampling and citation may bias the scientific consensuses that underpin attempts to accurately predict and effectively mitigate climate change in the region. Further work is required to increase both the quality and quantity of sampling, and incorporate existing literature from poorly cited areas to generate a more representative picture of Arctic climate change and its environmental impacts.

The majority of the northern peatlands developed during the Holocene as a result of a positive mass balance between net primary productivity (NPP) and heterotrophic decomposition rates. Over that time they have sequestered a huge amount of carbon in terrestrial ecosystems. A significant proportion of these areas also coincides with areas underlain with permafrost and shows a diverse range of peat accumulation patterns. Thus, for predicting and understanding the long-term evolution of peatland carbon stocks across the pan-Arctic, mechanistic representations of both peatland and permafrost dynamics are needed in the modelling framework. In this thesis, a novel implementation of dynamic multi-layer peatland and permafrost dynamics in the individual- and patch- based dynamic vegetation and ecosystem model (LPJ-GUESS) is described. The major emphasis of this work goes into enhancing the current understanding of the processes involved in the long-term peat accumulation and its internal dynamics, including how these systems are influenced by small-scale heterogeneity, vegetation dynamics and interactions with underlying permafrost. A simple two-dimensional microtopographical (2-DMT) model was also developed to address the established hypotheses concerning stability, behaviour and transformation of these microstructures and the effects of this small-scale heterogeneity on the coupled dynamics of vegetation, hydrology and peat accumulation. LPJ-GUESS was calibrated and validated using data from a mire in Stordalen, northern Sweden, and evaluated using data from multiple sites in Scandinavia and from Mer Bleue, Canada. It was subsequently applied across the pan-Arctic to advance the existing knowledge on carbon accumulation rates at different spatial and temporal scales, and also to demonstrate the potential implications of current warming on these climate sensitive ecosystems. Both of the models developed in this thesis performed satisfactorily when confronted with experimen

Popular summary Changes in climate modify the terrestrial vegetation distribution that in turn affects the climate system by altering the balance of radiation, water, momentum, CO2 and other important atmospheric gases. This entire process is termed as vegetation-climate feedbacks. These feedbacks are studied by regional earth system model. In this study, RCA-GUESS, a regional earth system model, is applied over India to capture the influence of these feedbacks on the present climate. I also used a regional climate model, RCA and compared its results with RCA GUESS. However, before applying these regional models to a large area such as India, it is important to validate these models. Therefore, the aim of this study is to first assess the performance of RCA GUESS and RCA, a regional climate model, against some observation datasets for the period 1989-2005. Later, this study examines the influence of vegetation feedbacks on present climate of India. At the end, the future climate predictions will be made using RCA. The study found that RCA was not accurately simulating average surface temperature and precipitation for summer as well as for winter months. On the other hand, the regional earth system model, RCA GUESS also showed similar pattern of deviations but the irregularities in this model are explained to a certain extent over some regions by including dynamic vegetation and in some cases, the deviations were even amplified. It is suggested that both models require significant improvement in their structural design for correctly representing the climate and vegetation pattern for tropical region. The study also discussed the reasons of significant deviations present over mountainous regions which was also noticed when these models were applied over different regions such as Europe and South America. Since both models showed considerable uncertainty in predicting the present climate variables, therefore, it is very hard to deduce the actual impact of climate on ve, Scientific summary There is a bidirectional relation between climate and vegetation distribution at local as well as global scale. Changes in climate pattern modify the terrestrial vegetation distribution that in turn affects the climate system by altering the balance of radiation, water, momentum, CO2 and other important atmospheric gases. The large-scale changes in vegetation structure and composition give rise to important climate relevant feedbacks (biophysical as well as biogeochemical). General circulation models that simulate future climate state do not take into account these interactions between land and atmosphere. Results from fairly recent studies bring forth the importance of including vegetation dynamics in order to predict the future climate more realistically. The physical changes in vegetation pattern such as changes in albedo, evapotranspiration, and surface roughness affect the climate pattern (i.e. through biophysical feedbacks) at regional scales, emphasizing the importance of coupling regional climate models with dynamic vegetation models. However, before applying the regional models to a large domain such as India, it is important to validate these models. Therefore, the aim of this study is to first assess the performance of RCA GUESS, a regional earth system model and RCA, a regional climate model, against observation datasets for the period 1989-2005. Later, the study examines the effect of vegetation feedbacks, more precisely biophysical feedbacks, on present climate of India. At the end, the future climate predictions will be made using RCA under IPCC’s future greenhouse gas scenario (SRES-A1B). The study found that RCA did not simulate average surface temperature and precipitation for summer as well as winter months quite reasonably. On the other hand, RCA GUESS also showed similar pattern of deviations but the biases in RCA GUESS are explained to a certain extent over some regions by including transient vegetation and in some cases the