Evaluation of land surface models in reproducing satellite derived leaf area index over the high-latitude northern hemisphere. Part II: Earth system models

PublishedJournal ArticleLeaf Area Index (LAI) is a key parameter in the Earth System Models (ESMs) since it strongly affects land-surface boundary conditions and the exchange of matter and energy with the atmosphere. Observations and data products derived from satellite remote sensing are important for the validation and evaluation of ESMs from regional to global scales. Several decades' worth of satellite data products are now available at global scale which represents a unique opportunity to contrast observations against model results. The objective of this study is to assess whether ESMs correctly reproduce the spatial variability of LAI when compared with satellite data and to compare the length of the growing season in the different models with the satellite data. To achieve this goal we analyse outputs from 11 coupled carbon-climate models that are based on the set of new global model simulations planned in support of the IPCC Fifth Assessment Report. We focus on the average LAI and the length of the growing season on Northern Hemisphere over the period 1986-2005. Additionally we compare the results with previous analyses (Part I) of uncoupled land surface models (LSMs) to assess the relative contribution of vegetation and climatic drivers on the correct representation of LAI. Our results show that models tend to overestimate the average values of LAI and have a longer growing season due to the later dormancy. The similarities with the uncoupled models suggest that representing the correct vegetation fraction with the associated parameterizations; is more important in controlling the distribution and value of LAI than the climatic variables. © 2013 by the authors.This work was funded by the European Commission’s 7th Framework Programme under Grant Agreements number 238366 (GREENCYCLESII project) and 282672 (EMBRACE project)

Uncertainties in CMIP5 climate projections due to carbon cycle feedbacks

Final published version of article.© 2014 American Meteorological SocietyIn the context of phase 5 of the Coupled Model Intercomparison Project, most climate simulations use prescribed atmospheric CO2 concentration and therefore do not interactively include the effect of carbon cycle feedbacks. However, the representative concentration pathway 8.5 (RCP8.5) scenario has additionally been run by earth system models with prescribed CO2 emissions. This paper analyzes the climate projections of 11 earth system models (ESMs) that performed both emission-driven and concentration-driven RCP8.5 simulations.When forced by RCP8.5 CO2 emissions, models simulate a large spread in atmospheric CO2; the simulated 2100 concentrations range between 795 and 1145 ppm. Seven out of the 11 ESMs simulate a larger CO2 (on average by 44 ppm, 985 ± 97ppm by 2100) and hence higher radiative forcing (by 0.25Wm-2) when driven by CO2 emissions than for the concentration-driven scenarios (941 ppm). However, most of these models already overestimate the present-day CO2, with the present-day biases reasonably well correlated with future atmospheric concentrations' departure from the prescribed concentration. The uncertainty in CO2 projections is mainly attributable to uncertainties in the response of the land carbon cycle. As a result of simulated higher CO2 concentrations than in the concentration-driven simulations, temperature projections are generally higher when ESMs are driven with CO2 emissions. Global surface temperature change by 2100 (relative to present day) increased by 3.9° ± 0.9°C for the emission-driven simulations compared to 3.7° ± 0.7°C in the concentration-driven simulations. Although the lower ends are comparable in both sets of simulations, the highest climate projections are significantly warmer in the emission-driven simulations because of stronger carbon cycle feedbacks. © 2014 American Meteorological Society.Department for Environment, Food and Rural Affairs (DEFRA)Department of Energy & Climate Change (DECC

The carbon cycle in Mexico: past, present and future of C stocks and fluxes

PublishedThe Supplement related to this article is available online at doi:10.5194/bg-13-223-2016-supplement.We modeled the carbon (C) cycle in Mexico with a process-based approach. We used different available products (satellite data, field measurements, models and flux towers) to estimate C stocks and fluxes in the country at three different time frames: present (defined as the period 2000–2005), the past century (1901–2000) and the remainder of this century (2010–2100). Our estimate of the gross primary productivity (GPP) for the country was 2137 ± 1023 TgC yr−1 and a total C stock of 34 506 ± 7483 TgC, with 20 347 ± 4622 TgC in vegetation and 14 159 ± 3861 in the soil. Contrary to other current estimates for recent decades, our results showed that Mexico was a C sink over the period 1990–2009 (+31 TgC yr−1) and that C accumulation over the last century amounted to 1210 ± 1040 TgC. We attributed this sink to the CO2 fertilization effect on GPP, which led to an increase of 3408 ± 1060 TgC, while both climate and land use reduced the country C stocks by −458 ± 1001 and −1740 ± 878 TgC, respectively. Under different future scenarios, the C sink will likely continue over the 21st century, with decreasing C uptake as the climate forcing becomes more extreme. Our work provides valuable insights on relevant driving processes of the C cycle such as the role of drought in drylands (e.g., grasslands and shrublands) and the impact of climate change on the mean residence time of soil C in tropical ecosystems.The lead author (G. Murray-Tortarolo) thanks CONACYT-CECTI, the University of Exeter and Secretaría de Educación Pública (SEP) for their funding of this project. The authors extend their thanks to Carlos Ortiz Solorio and to the Colegio de Posgraduados for the field soil data and to the Alianza Redd+ Mexico for the field biomass data. This project would not have been possible without the valuable data from the CMIP5 models. A. Arneth, G. Murray-Tortarolo, A. Wiltshire and S. Sitch acknowledge the support of the European Commission-funded project LULCC4C (grant no. 603542). A. Wiltshire was partsupported by the Joint UK DECC/Defra Met Office Hadley Centre Climate Programme (GA01101)

Impacts of air pollution on human and ecosystem health, and implications for the National Emission Ceilings Directive. Insights from Italy

Across the 28 EU member states there were nearly half a million premature deaths in 2015 as a result of exposure to PM2.5, O3 and NO2. To set the target for air quality levels and avoid negative impacts for human and ecosystems health, the National Emission Ceilings Directive (NECD, 2016/2284/EU) sets objectives for emission reduction for SO2, NOx, NMVOCs, NH3 and PM2.5 for each Member State as percentages of reduction to be reached in 2020 and 2030 compared to the emission levels into 2005. One of the innovations of NECD is Article 9, that mentions the issue of “monitoring air pollution impacts” on ecosystems. We provide a clear picture of what is available in term of monitoring network for air pollution impacts on Italian ecosystems, summarizing what has been done to control air pollution and its effects on different ecosystems in Italy. We provide an overview of the impacts of air pollution on health of the Italian population and evaluate opportunities and implementation of Article 9 in the Italian context, as a case study beneficial for all Member States. The results showed that SO42− deposition strongly decreased in all monitoring sites in Italy over the period 1999–2017, while NO3− and NH4+ decreased more slightly. As a consequence, most of the acid-sensitive sites which underwent acidification in the 1980s partially recovered. The O3 concentration at forest sites showed a decreasing trend. Consequently, AOT40 (the metric identified to protect vegetation from ozone pollution) showed a decrease, even if values were still above the limit for forest protection (5000 ppb h−1), while PODy (flux-based metric under discussion as new European legislative standard for forest protection) showed an increase. National scale studies pointed out that PM10 and NO2 induced about 58,000 premature deaths (year 2005), due to cardiovascular and respiratory diseases. The network identified for Italy contains a good number of monitoring sites (6 for terrestrial ecosystem monitoring, 4 for water bodies monitoring and 11 for ozone impact monitoring) distributed over the territory and will produce a high number of monitored parameters for the implementation of the NECD

Enhancing multi-asset portfolio performances with market timing using the Vix

The ‘Fear gauge’, more commonly known as the VIX, or Market Volatility Index of the Chicago Board Options Exchange, is a popular market indicator which translates the degree of uncertainty in the market. This paper aims to exploit market timing using the VIX to enhance multi-asset portfolios performance. The strategy provides better performance than a simple risk-parity approach and works as a great diversifier of risk. Stocks/Bonds relationship and Style timing are both used effectively to enhance returns. However, gold fails to hedge against high volatility and other assets such as cash might be better options

Evaluating the land and ocean components of the global carbon cycle in the CMIP5 earth system models

PublishedJournal ArticleThe authors assess the ability of 18 Earth system models to simulate the land and ocean carbon cycle for the present climate. These models will be used in the next Intergovernmental Panel on Climate Change (IPCC) Fifth AssessmentReport (AR5) for climate projections, and such evaluation allows identification of the strengths and weaknesses of individual coupled carbon-climate models as well as identification of systematic biases of themodels. Results show thatmodels correctly reproduce the main climatic variables controlling the spatial and temporal characteristics of the carbon cycle. The seasonal evolution of the variables under examination is well captured. However, weaknesses appear when reproducing specific fields: in particular, considering the land carbon cycle, a general overestimation of photosynthesis and leaf area index is found for most of the models, while the ocean evaluation shows that quite a few models underestimate the primary production. The authors also propose climate and carbon cycle performance metrics in order to assess whether there is a set of consistently better models for reproducing the carbon cycle. Averaged seasonal cycles and probability density functions (PDFs) calculated from model simulations are compared with the corresponding seasonal cycles and PDFs from different observed datasets. Although the metrics used in this study allow identification of somemodels as better or worse than the average, the ranking of this study is partially subjective because of the choice of the variables under examination and also can be sensitive to the choice of reference data. In addition, it was found that the model performances show significant regional variations. © 2013 American Meteorological Society.This work was supported by the European Commission's 7th Framework Programme under Grant Agreements 238366 (GREENCYCLESII) and 282672 (EMBRACE), while Dr. Jones was supported by the Joint DECC/Defra Met Office Hadley Centre Climate Program (GA01101)

Using RL to Predict Crypto and the effect of COVID-19

The stock market has always been subject to speculation. Oftentimes it is volatile but it is still an important mode of investment for many people. It carries great weight and is reflective of the performance of an economy. In macroeconomic courses in college, students are taught that in times of crises, CASH is KING, not assets in the market. Naturally, one would assume the COVID-19 pandemic would cause people to flock to holding monetary assets in cash. However, recently, as a result of the pandemic there has been a rise in the demand for cryptocurrencies. This might be because people expected the government to inject money into the economy in order to stimulate demand, and therefore people expected a rise in inflation and a fall in the value of money. So they took to a form of currency that doesn’t fall under governmental control. Be it Bitcoin or Ethereum or even the rise in ”meme coins” such as DOGE. There has been a shift in how economics predicts how people behave