On the impact of canopy model complexity on simulated carbon, water, and solar-induced chlorophyll fluorescence fluxes

Lack of direct carbon, water, and energy flux observations at global scales makes it difficult to calibrate land surface models (LSMs). The increasing number of remote-sensing-based products provide an alternative way to verify or constrain land models given their global coverage and satisfactory spatial and temporal resolutions. However, these products and LSMs often differ in their assumptions and model setups, for example, the canopy model complexity. The disagreements hamper the fusion of global-scale datasets with LSMs. To evaluate how much the canopy complexity affects predicted canopy fluxes, we simulated and compared the carbon, water, and solar-induced chlorophyll fluorescence (SIF) fluxes using five different canopy complexity setups from a one-layered canopy to a multi-layered canopy with leaf angular distributions. We modeled the canopy fluxes using the recently developed land model by the Climate Modeling Alliance, CliMA Land. Our model results suggested that (1) when using the same model inputs, model-predicted carbon, water, and SIF fluxes were all higher for simpler canopy setups; (2) when accounting for vertical photosynthetic capacity heterogeneity, differences between canopy complexity levels increased compared to the scenario of a uniform canopy; and (3) SIF fluxes modeled with different canopy complexity levels changed with sun-sensor geometry. Given the different modeled canopy fluxes with different canopy complexities, we recommend (1) not misusing parameters inverted with different canopy complexities or assumptions to avoid biases in model outputs and (2) using a complex canopy model with angular distribution and a hyperspectral radiation transfer scheme when linking land processes to remotely sensed spectra.

A Brief Study on the Achievement of Syukuro Manabe, Awardee of 2021 Nobel Prize in Physics

Manabe Syukuro is well known as a father of climate modeling. He and his colleagues have achieved several important milestones in the research on global warming. In this article, two highly advanced subjects are described. In the early 1960s, he developed a radiative-convective model of the atmosphere and explored the role of greenhouse gases, such as water vapor, carbon dioxide, and ozone in maintaining and changing the thermal structure of the atmosphere. His study was the beginning of long-term research on global warming. In 1969, Manabe and Bryan published the first results from a coupled ocean-atmosphere general circulation model (OAGCM). However, this model used a highly idealized continent-ocean configuration. Results from the first coupled OAGCM with more realistic configurations were published in 1975, which eventually became a very powerful tool for the simulation of global warming.

Credibility via Coupling

This study investigates Model Intercomparison Projects (MIPs) as one example of a coordinated approach to establishing scientific credibility. MIPs originated within climate science as a method to evaluate and compare disparate climate models, but MIPs or MIP-like projects are now spreading to many scientific fields. Within climate science, MIPs have advanced knowledge of: a) the climate phenomena being modeled, and b) the building of climate models themselves. MIPs thus build scientific confidence in the climate modeling enterprise writ large, reducing questions of the credibility or reproducibility of any single model. This paper will discuss how MIPs organize people, models, and data through institution and infrastructure coupling (IIC). IIC involves establishing mechanisms and technologies for collecting, distributing, and comparing data and models (infrastructural work), alongside corresponding governance structures, rules of participation, and collaboration mechanisms that enable partners around the world to work together effectively (institutional work). Coupling these efforts involves developing formal and informal ways to standardize data and metadata, create common vocabularies, provide uniform tools and methods for evaluating resulting data, and build community around shared research topics.

Epidemic waves in relief against a structured climatic background

<p>For directly transmissible infectious diseases, seasonality in the course of epidemics may manifest in four major ways: susceptibility of the hosts, their individual and collective behavior, transmissibility of the pathogen, and survival of the latter under evolving environmental conditions. Mechanisms and concepts are not finally settled, notably in a pandemic setting. Climatic seasonality by itself is an aggregate, structured phenomenon that provides a spatially distributed background to the epidemic outbreak and its evolution at multiple timescales. Using advanced methods of data and systems analysis, including epidemiological and climate modeling, the RKI data of the COVID-19 epidemic curve for Berlin and a five-parameter climate data set of the nearby station Lindenberg (Mark) are analyzed in daily resolution over the period March 2020 to October 2021. Aimed to identify extrinsic impacts due to organized intraseasonal climate dynamics, as seen in sunshine duration and surface climate (pressure, temperature, humidity, wind), on intrinsic dynamics of the epidemic system, an established (SEIR) model of the latter and modifications thereof have been subjected to in-depth studies with a view on both genesis and timing of epidemic waves and their potential synchronization with climatic background dynamics. Starting with a case study for the spring 2020 period of shutdown, which unveils remarkable synchronies with the seasonal transition, estimates are given and applied to the follow-up period of individual and combined impacts of climate variables on the SEIR model in different oscillatory (non-equilibrium, lately endemic) regimes of operation.</p>

Elevation of the Gangdese Mountains and Their Impacts on Asian Climate During the Late Cretaceous—a Modeling Study

Uplift of the Gangdese Mountains is important to the evolution of Asian monsoons and the formation of Tibetan Plateau, but its paleoaltitude before the India-Asia collision (Late Cretaceous) is less constrained so far. In this study, we investigate whether the geological records, which are indicators of soil dryness, discovered in East Asia can provide such a constraint. Through climate modeling using the Community Earth System Model version 1.2.2, it is found that the extent of dry land in East Asia is sensitive to the altitude of the Gangdese Mountains. It expands eastwards and southwards with the rise of the mountain range. Comparison of the model results with all the available geological records in this region suggests that the Gangdese Mountains had attained a height of ∼2 km in the Late Cretaceous.

Reliability Assessment of Regional Climate Modeling: Case study in Johor State, Malaysia

This paper presents the work of statistically downscaling the CAN ESM 2 (Canada Earth System Model 2) climate data into regional climate data to produce the future climate scenario using the RCP (Representative Concentration Pathways) 2.6,4.5 and 8.5 green-house gas concentration trajectory suggested by Intergovernmental Panel on Climate Change Fifth Assessment Reports (IPCC-AR5). Selected location for regional climate downscaling includes Batu Pahat (1° 52’ N 102° 59’ E) and Kulai (1° 38’ N 103° 40’ E), downscaled outcome of monthly rainfall (mm), daily maximum (Tmax) and daily minimum (Tmin) temperature (°C) was produced by using SDSM (Statistical Downscaling Model) software to calibrate the CANESM2 output with the historical data. Quantile-mapping bias correction by using exponential distribution function was done to obtain bias corrected rainfall data. Reliability test using Pearson correlation coefficient was done by comparing between actual historical data. Based on Pearson correlation applied on bias corrected results, for Batu Pahat, the most suitable RCP model for both Tmax and Tmin is RCP 2.6, with correlation of 0.74 and 0.72, most suitable model for rainfall is RCP 4.5 with correlation of 0.24. For Kulai, the most suitable RCP model for Tmin is RCP 8.5, with correlation of 0.63, for Tmax and rainfall the suitable model is RCP 2.6, with correlation of 0.73 and 0.36. In overall, the more appropriate model to describe the climate for both Batu Pahat and Kulai based on Pearson correlation from year 2006 to 2019 is RCP 2.6, as the RCP 2.6 model are having higher correlation to the historical data.

Characterization of vegetation fraction estimated using spot-vegetation NDVI data for regional climate modeling in India

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