This paper reports the progress of our efforts to apply the livestock weight model developed by ourselves in the past to future scenarios. First, I analyzed the results of future scenario experiments ...of global climate models that incorporate biogeochemical cycles including ecosystems (e.g., Earth system model), and confirmed that the amount of vegetation around Mongolia will basically be projected to increase, and that the degree of this increase is more emphasized in the high temperature scenarios. This can be attributed to the effects of carbon dioxide fertilization, higher temperatures, and higher nitrogen concentrations by fertilizers etc. Next, to prepare input data for the livestock weight model, I presented the results of attempting downscaling method combining an offline vegetation model and a convolutional neural network. The former method achieved a spatial resolution of 0.5°×0.5°, which could be further refined to 8 km×8 km by the latter method. Although some technical problems remain, the results showed the possibility of obtaining future vegetation distribution with sufficient spatial resolution for analyzing nomadism. Finally, using the Leaf Area Index (LAI) distribution for 2021-2030 with RCP 8.5 scenario derived by the above method, and given the assumption that livestock move to the grid with the largest LAI value in the surrounding area each month, the LAI of the grid where livestock stay was calculated. Here, the average LAI for the year preceding the month of April, when livestock weight drops, was presented. In the future, I intend to further improve the downscaling method and the livestock weight model.
The transient climate response to cumulative carbon emissions (TCRE) is a key metric in estimating the remaining carbon budget for given temperature targets. However, the TCRE has a small scenario ...dependence that can be non-negligible for stringent temperature targets. To investigate the parametric correlations and scenario dependence of the TCRE, the present study uses a 512-member ensemble of an Earth system model of intermediate complexity (EMIC) perturbing 11 physical and biogeochemical parameters under scenarios with steady increases of 0.25%, 0.5%, 1%, 2%, or 4% per annum (ppa) in the atmospheric CO
2
concentration (pCO
2
), or an initial increase of 1% followed by an annual decrease of 1% thereafter. Although a small difference of 5% (on average) in the TCRE is observed between the 1-ppa and 0.5-ppa scenarios, a significant scenario dependence is found for the other scenarios, with a tendency toward large values in gradual or decline-after-a-peak scenarios and small values in rapidly increasing scenarios. For all scenarios, correlation analysis indicates a remarkably large correlation between the equilibrium climate sensitivity (ECS) and the relative change in the TCRE, which is attributed to the longer response time of the high ECS model. However, the correlations of the ECS with the TCRE and its scenario dependence for scenarios with large pCO
2
increase rates are slightly smaller, and those of biogeochemical parameters such as plant respiration and the overall pCO
2
–carbon cycle feedback are larger, than in scenarios with gradual increases. The ratio of the TCREs under the overshooting (i.e., 1-ppa decrease after a 1-ppa increase) and 1-ppa increase only scenarios had a clear positive relation with zero-emission commitments. Considering the scenario dependence of the TCRE, the remaining carbon budget for the 1.5 °C target could be reduced by 17 or 22% (before and after considering the unrepresented Earth system feedback) for the most extreme case (i.e., the 67
th
percentile when using the 0.25-ppa scenario as compared to the 1-ppa increase scenario). A single ensemble EMIC is also used to indicate that, at least for high ECS (high percentile) cases, the scenario dependence of the TCRE should be considered when estimating the remaining carbon budget.
For the purpose of identifying the key processes and sectors involved in the interaction between Earth and socio-economic systems, we review existing studies on those processes/sectors through which ...the climate impacts socio-economic systems, which then in turn affect the climate. For each process/sector, we review the direct physical and ecological impacts and, if available, the impact on the economy and greenhouse gas (GHG) emissions. Based on this review, land sector is identified as the process with the most significant impact on GHG emissions, while labor productivity has the largest impact on the gross domestic product (GDP). On the other hand, the energy sector, due to the increase in the demand for cooling, will have increased GHG emissions. Water resources, sea level rise, natural disasters, ecosystem services, and diseases also show the potential to have a significant influence on GHG emissions and GDP, although for most of these, a large effect was reported only by a limited number of studies. As a result, more studies are required to verify their influence in terms of feedbacks to the climate. In addition, although the economic damage arising from migration and conflict is uncertain, they should be treated as potentially damaging processes.
The past 20 years of research using Earth system models (ESMs) is reviewed with an emphasis on results from the ESM based on MIROC (Model for Interdisciplinary Research on Climate) developed in ...Japan. Earth system models are climate models incorporating biogeochemical processes such as the carbon cycle. The development of ESM was triggered by studies of the feedback between climate change and the carbon cycle. State-of-the-art ESMs are much more realistic than the first ESMs. They now include various biogeochemical processes other than carbon, such as atmospheric chemistry and the nitrogen and iron cycles as well as nutrient transport by atmospheric dust and rivers. They are used to address many practical issues, such as evaluating the amount of carbon dioxide emissions that is consistent with climate change mitigation targets, and are indispensable tools for the development of climate change mitigation policies. Novel, ambitious attempts to use ESMs include coupling socioeconomics with Earth systems, and projecting the carbon cycle on decadal timescales. Development of ESMs requires ongoing integration of multiple aspects of climate science. Emerging applications of ESMs can bring forth meaningful insights, and should be directed toward expanding connections with fields outside climate science, e.g., socioeconomics.
To evaluate uncertainty in the transient climate response (TCR) associated with microscale deep-ocean mixing processes induced by internal tidal wave breaking, a set of idealized climate model ...experiments with two different implementations of deep-ocean mixing is conducted under increasing atmospheric CO2 concentration 1% per year. The difference in TCR between the two experiments is 0.16 °C, which is about half as large as the multimodel spread of TCR in the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. The TCR difference can be attributed to the difference in the preindustrial climatological state. In the case where deep-ocean mixing works to enhance ocean stratification in the Pacific intermediate-to-deep layers, because the Pacific water mass is transported to the Southern Ocean by the Pacific meridional overturning circulation, the subsurface stratification in the Southern Ocean is also enhanced and deep wintertime convection there is suppressed. Our study shows that in this case during CO2 increase, ocean heat uptake from the atmosphere to deeper layers is suppressed and TCR is estimated to be higher than the other case. Diminished accumulation of oceanic heat in the deep layer also leads to the sea level depression of ∼0.4 m in the Southern Ocean when atmospheric CO2 concentration has quadrupled. Together with convective and cloud-radiative processes in the atmosphere and oceanic mesoscale processes, microscale deep-ocean mixing can be one of the major candidates in explaining uncertainty in future climate projections.
The Global Change Observation Mission-Climate (GCOM-C), launched in 2017, has suitable bands matching the photochemical reflectance index (PRI) definition. It also has the bands for the normalized ...difference vegetation index (NDVI). The PRI has a unique capability to detect plant stress caused by excessive light and drought. However, no moderate-resolution satellites had suitable bands for the PRI, requiring two narrow bands in green light in the definition. In this study, we conducted the early validation study of PRI and NDVI derived from the GCOM-C satellite and demonstrated those accuracies and characteristics in Mongolian grassland. The Mongolian Steppes (dry grasslands) are widely distributed on the plateau and therefore suitable for satellite validation. It is particularly suitable for the PRI validation because Mongolian grasslands have water stress due to the small amount of precipitation in summer. Therefore, we carried out field campaigns at three study sites in Mongolia. In this study, we found the seasonal pattern of PRI suggesting the potential to detect the water stress of vegetation, which is essential information for informed management of the grasslands. However, the correlation between the satellite-derived PRI and the in-situ PRI was negative because of the dependence of GCOM-C PRI on the soil moisture at sparse vegetation. For the accuracy assessment of PRI, which depends on rapidly changing light and soil moisture in a day, more exact synchronization of in-situ and satellite observation is required. On the other hand, we found that the NDVI derived from GCOM-C was highly accurate: The correlation coefficient (R) between the satellite-derived NDVI and the in-situ NDVI was 0.988 (RMSE=0.052). GCOM-C NDVI has enough similarities with MODIS NDVI in terms of accuracy, spatial resolution, and frequency. For example, we demonstrated that GCOM-C NDVI could detect the phenology with the same or better accuracy than MODIS NDVI. We also demonstrated their difference: the soil moisture dependence in sparse vegetation. The less dependency of GCOM-C NDVI on the soil moisture leads to a better classification of vegetation and non-vegetation in the sparse grassland than MODIS NDVI.
An Earth system model (ESM) was used to investigate the effect of reaching the target of 1.5 °C warming (relative to preindustrial levels) after overshooting to the 2 °C level with respect to ...selected global environment indicators. Two scenarios were compared that diverged after reaching the 2 °C level: one stayed at the 2 °C level, and the other cooled to the 1.5 °C level. Unlike the internationally coordinated model intercomparison projects, the scenarios were developed for a specific climatic model with emissions and land use scenarios consistent with socioeconomic projections from an integrated assessment model. The ESM output resulted in delayed realization of the 1.5 °C and 2 °C targets expected for 2100. The cumulative CO2 emissions for 2010−2100 (2300) were 358 (−53) GtCO2 in the 2 °C scenario and −337 (−936) GtCO2 in the 1.5 °C scenario. We examined the effect of overshooting on commonly used indicators related to surface air temperature, sea surface temperature and total ocean heat uptake. Global vegetation productivity at 2100 showed around a 5% increase in the 2 °C scenario without overshooting compared with the 1.5 °C scenario with overshooting, considered to be caused by more precipitation and stronger CO2 fertilization. A considerable difference was found between the two scenarios in terms of Arctic sea ice, whereas both scenarios indicated few corals would survive past the 21st century. The difference in steric sea level rise, reflecting total cumulative ocean heat uptake, between the two scenarios was <2 cm in 2100, and around 9 cm in 2300 in the Pacific Island region. A large overshoot may reduce the eventual difference between targets (i.e. 1.5 °C in contrast to 2 °C), particularly in terms of the indicators related to total ocean heat uptake, and to sensitive biological thresholds.
The evolution of the Atlantic Meridional Overturning Circulation (MOC) in 30 models of varying complexity is examined under four distinct Representative Concentration Pathways. The models include 25 ...Atmosphere‐Ocean General Circulation Models (AOGCMs) or Earth System Models (ESMs) that submitted simulations in support of the 5th phase of the Coupled Model Intercomparison Project (CMIP5) and 5 Earth System Models of Intermediate Complexity (EMICs). While none of the models incorporated the additional effects of ice sheet melting, they all projected very similar behaviour during the 21st century. Over this period the strength of MOC reduced by a best estimate of 22% (18%–25%; 5%–95% confidence limits) for RCP2.6, 26% (23%–30%) for RCP4.5, 29% (23%–35%) for RCP6.0 and 40% (36%–44%) for RCP8.5. Two of the models eventually realized a slow shutdown of the MOC under RCP8.5, although no model exhibited an abrupt change of the MOC. Through analysis of the freshwater flux across 30°–32°S into the Atlantic, it was found that 56% of the CMIP5 models were in a bistable regime of the MOC for at least part of their RCP integrations. The results support previous assessments that it is very unlikely that the MOC will undergo an abrupt change to an off state as a consequence of global warming.
Key Points
All climate models project very similar behavior during the 21st century
No model exhibits an abrupt change of the MOC
More than 1/2 of the models are in the bistable regime ==> not overly stable
Carbon uptake by land and ocean as a biogeochemical response to increasing atmospheric CO₂ concentration is called concentration–carbon feedback and is one of the carbon cycle feedbacks of the global ...climate. This feedback can have a major impact on climate projections with an uncertain magnitude. This paper focuses on the concentration–carbon feedback in terrestrial ecosystems, analyzing the mechanisms and strength of the feedback reproduced by Earth system models (ESMs) participating in phase 5 of the Coupled Model Intercomparison Project. It is confirmed that multiple ESMs driven by a common scenario show a large spread of concentration–carbon feedback strength among models. Examining the behavior of the carbon fluxes and pools of the models showed that the sensitivity of plant productivity to elevated CO₂ is likely the key to reduce the spread, although increasing CO₂ stimulates other carbon cycle processes. Simulations with a single ESM driven by different CO₂ pathways demonstrated that carbon accumulation increases in scenarios with slower CO₂ increase rates. Using both numerical and analytical approaches, the study showed that the difference among CO₂ scenarios is a time lag of terrestrial carbon pools in response to atmospheric CO₂ increase—a high rate of CO₂ increase results in smaller carbon accumulations than that in an equilibriumstate of a given CO₂ concentration. These results demonstrate that the current quantities for concentration–carbon feedback are incapable of capturing the feedback dependency on the carbon storage state and suggest that the concentration feedback can be larger for future scenarios where the CO₂ growth rate is reduced.
Celotno besedilo
Dostopno za:
BFBNIB, DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Recent climate modeling studies have concluded that cumulative carbon emissions determine temperature increase, regardless of emission pathways. Accordingly, the optimal emission pathway can be ...determined from a socioeconomic standpoint. To access the path dependence of socioeconomic impacts for cumulative carbon emissions, we used a computable general equilibrium model to analyze impacts on major socioeconomic indicators on a global scale for 30–50 pathways with different emission reduction starting years, different subsequent emission pathways, and three different cumulative 2100 emission scenarios (emissions that meet the 2 °C target, the 2 °C target emissions plus 10 %, and emissions producing radiative forcing of 4.5 W/m
2
). The results show that even with identical cumulative emission figures, the resulting socioeconomic impacts vary by the pathway realized. For the United Nations 2 °C target, for example, (a) the 95 % confidence interval of cumulative global gross domestic product (GDP) is 1355–1363 trillion US dollars (2010–2100, discount rate = 5 %), (b) the cumulative GDP of pathways with later emission reduction starting years grows weaker (5 % significance level), and (c) emissions in 2100 have a moderate negative correlation with cumulative GDP. These results suggest that GDP loss is minimized with pathways with earlier emission reduction followed by more moderate reduction rates to achieve lower emission levels. Consequently, we suggest an early emission peak to meet the stringent target. In our model setting, it is desirable for emissions to peak by 2020 to reduce mitigation cost and by 2030 at the latest to meet the 2 °C target.
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