Fires impact a suite of radiative forcing agents but fire is one of the most challenging sources of emissions to model due to a large degree of stochasticism and a wide range of climatic and human influences that can both increase and decrease the occurrence of fires. Although many Earth system models now account for fires, there is still a need for a coherent and consistent community dataset to intercompare and constrain models. We developed a historic dataset combining satellite data over the past two decades with proxy data and fire models for use in CMIP6. Since then, new satellite data has indicated that global burned area may be much higher than previously thought and several regional datasets have shed light on the question whether fire emissions are now higher or not than in the pre-industrial era. We show how the latest insight and developments will be used to construct an updated fire emissions dataset for CMIP7, and show which fire categories carry the largest uncertainty, both for the past and into the future.

How to cite: van der Werf, G. and van Marle, M.: Biomass burning emissions since the pre-industrial and into the future; progress and challenges, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-9994, https://doi.org/10.5194/egusphere-egu24-9994, 2024.

Modelled water-mass changes in the North Pacific thermocline from CMIP6, both in the subsurface and at the surface, reveal the impact of the competition between anthropogenic aerosols and greenhouse gases (GHGs) over the past 6 decades. The aerosol effect overwhelms the GHG effect during 1950-1985 in driving salinity changes on density surfaces, while after 1985 the GHG effect dominates. These subsurface water-mass changes are traced back to changes at the surface, of which ~70% stems from the migration of density surface outcrops, equatorward due to regional cooling by anthropogenic aerosols and subsequent poleward due to warming by GHGs. Ocean subduction connects these surface outcrop changes to the main thermocline. Both observations and models reveal this transition in climate forcing around 1985 and highlight the important role of anthropogenic aerosol climate forcing on our oceans’ water masses.

How to cite: Shi, J.-R., Wijffels, S., Kwon, Y.-O., Talley, L., and Gille, S.: The Competition Between Anthropogenic Aerosol and Greenhouse Gas Forcing is Revealed by North Pacific Water-mass Changes, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-7042, https://doi.org/10.5194/egusphere-egu24-7042, 2024.

The solar forcing dataset prepared for the 6^th round of the Coupled Model Intercomparison Project (CMIP6) has been used extensively in climate model experiments. Recently, an International Space Science Institute (ISSI) Working Group was established to revisit the solar forcing recommendations in order to define a roadmap for building a revised solar forcing dataset for the upcoming 7^th round of CMIP (Funke et al., 2023). This new dataset will introduce changes in the radiative forcing of climate either directly, or indirectly via changes in atmospheric composition. In CMIP6, the solar forcing consisted of both a total solar irradiance (TSI), along with a spectrally resolved solar irradiance (SSI). The TSI for solar minimum was set to 1360.8±0.5Wm^-2 and the SSI covered the 10nm to 100mm spectral region. A similar approach is proposed for CMIP7 except for two major aspects of the reconstruction: 1) the definition of the reference spectrum for the quite Sun; 2) the temporal variability. The major difference between the proposed CMIP7 SSI quite sun reference spectrum and that used for CMIP6 is the spectral shape. The new SSI spectrum has an irradiance that is 1-5% higher in the visible band and lower by 1-2% in the Near-IR wavelength range (1000-2000nm). The solar temporal variability in the CMIP6 and CMIP7 reconstructions are based on both the NRLSSI2 and SATIRE reconstructions. These reconstructions have been improved in preparation for CMIP7 and the aim is for both reconstructions to use the same reference spectrum and be driven by the same solar proxies. In this work we used the Whole Atmosphere Community Climate Model (WACCM) to examine the chemical and climate implications of the proposed CMIP7 solar forcing updates compared to the CMIP6 approach. WACCM is a chemistry-climate model that extends from the surface to 140km. The horizontal resolution is ~1degree. WACCM has a detailed representation of chemical and dynamical processes from the troposphere through the lower thermosphere. We examined the “chemical only” impacts of the solar forcing choice by running WACCM in the specified dynamics mode using NASA Modern-Era Retrospective analysis for Research and Applications Version 2 (MERRA2). The “climate” impacts were derived by running the model with interactive dynamics coupled to a deep ocean. Conclusions from this work will support the development of the next version of WACCM for participation in the CMIP7 assessment.

Funke, B., Dudok de Wit, T., Ermolli, I., Haberreiter, M., Kinnison, D., Marsh, D., Nesse, H., Seppälä, A., Sinnhuber, M., and Usoskin, I.: Towards the definition of a solar forcing dataset for CMIP7, Geosci. Model Dev. Discuss. https://doi.org/10.5194/gmd-2023-100.

How to cite: Kinnison, D., Marsh, D., and Tilmes, S.: Evaluation of the chemistry and climate impact of the new solar forcing dataset for CMIP7 using the Whole Atmosphere Community Climate Model, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-6774, https://doi.org/10.5194/egusphere-egu24-6774, 2024.

Anthropogenic aerosol forcing is quantitatively uncertain affecting the ability to constrain the climate response to anthropogenic perturbations. Climate models participating in the Coupled Model Intercomparison Project (CMIP) use different methods to incorporate direct and cloud-mediated aerosol effects. Some models in CMIP6 used prescribed anthropogenic aerosol optical properties and associated effects on cloud droplet number concentrations from the Simple Plumes parameterization fitted to the Max-Planck-Institute for Meteorology’s Aerosol Climatology version 2 (MACv2-SP). MACv2-SP was originally designed for the use in a subset of experiments for the Radiative Forcing Model Intercomparison Project to better understand the model diversity in aerosol forcing (Fiedler et al., 2023). The final uptake of MACv2-SP for research was, however, much broader. In the context of CMIP, the implementation of MACv2-SP in several climate models led to the request for new MACv2-SP input data that are consistent with updated emissions, e.g., in the framework of CovidMIP (Fiedler et al., 2021) and now in preparation for CMIP7 via the CMIP Climate Forcings Task Team. Moreover, MACv2-SP also serves in creating seasonal and decadal predictions, and satellite products.

We will therefore derive and freely provide new data for the anthropogenic aerosol optical properties and their cloud-mediated effects based on newly available emissions. The next data version of MACv2-SP is currently in preparation for interests in using CMIP6plus compliant boundary data. It will use the historical emission data for aerosols and their precursors from the new release of the Community Emission Data System (CEDS), which will be published at the beginning of 2024. The new emissions will allow us to revise and extent the historical data for MACv2-SP to include years after 2014. Expected changes compared to the MACv2-SP data used in CMIP6 are improved aerosol optical depth over some land regions in recent years, where the observations developed differently compared to assumptions in the scenarios. We will further translate uncertainty in the emission data to expected differences in the aerosol forcing. In addition to the new data for CMIP6plus, a new development of the simple plumes approach will be made for an assessment of the radiative forcing and climate response to aerosols from severe wild fires in recent years that are not represented by CMIP6 models.

Fiedler, S., Wyser, K., Rogelj, J. and van Noije, T. (2021) Radiative effects of reduced aerosol emissions during the COVID-19 pandemic and the future recovery.  Atmospheric Research, 264 . Art.Nr. 105866. DOI 10.1016/j.atmosres.2021.105866.

Fiedler, S., van Noije, T., Smith, C. J., Boucher, O., Dufresne, J., Kirkevåg, A., Olivié, D., Pinto, R., Reerink, T., Sima, A. and Schulz, M. (2023) Historical Changes and Reasons for Model Differences in Anthropogenic Aerosol Forcing in CMIP6. Geophysical Research Letters, 50 (15). Art.Nr. e2023GL104848. DOI 10.1029/2023GL104848.

How to cite: Fiedler, S., Bischof, S., Sudarchikova, N., Hoesly, R. M., and Smith, S. J.: Anthropogenic aerosol forcing in CMIP from prescribed optical and cloud microphysical properties, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-5200, https://doi.org/10.5194/egusphere-egu24-5200, 2024.

Given the significant socioeconomic impact of the East Asian Summer Monsoon (EASM), a critical area of investigation involves comprehending how the EASM and, consequently, the hydrological cycle over East Asia might change in future climates. To address this inquiry, reliable climate models must be employed. While assessments of model performance commonly concentrate on the generated precipitation amounts during the EASM period, it is important to note that the representation of dynamical components such as the Mei-yu front (MYF) are not frequently investigated. As model outputs may be correct for incorrect reasons, the dynamical components of the EASM might be misrepresented.
In this investigation, we scrutinized the representation of the MYF in historical simulations of 38 CMIP6 models from May to August, comparing them to ERA5. Our findings reveal that numerous CMIP6 models encounter difficulties in reproducing the climatology of the MYF similar to observations. By sub-sampling models based on the meridional position bias of the MYF in May, we identified distinct monthly variations within these groupings. Additionally, the origins of these biases were examined. Our study stresses the link between the misrepresentation of MYF climatology in CMIP6 models and the depiction of the North Pacific High, particularly its western edge. The implications of these discoveries are also explored. 

How to cite: Leckebusch, G. C., Ng, K. S., and Hodges, K. I.: Climatological Evaluation of the Mei-yu Front Representation in CMIP6, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-1657, https://doi.org/10.5194/egusphere-egu24-1657, 2024.

Reconstructions of past volcanic forcing rely on the assumption that the stratospheric sulphur loading from eruptions in the pre-satellite era is directly proportional to the sulphate flux recorded in polar ice sheets. The scaling factors, known as "transfer functions," used for this calculation are currently based on the Antarctic sulphate flux following the 1991 Pinatubo eruption, radioactivity in Greenland ice from nuclear weapon tests, and model simulations of two high-latitude eruptions. However, recent studies have shown that ice sheet deposition of volcanic sulphate varies significantly as a function of both eruptive parameters and the background atmospheric state, presenting an opportunity to enhance the accuracy and reliability of volcanic forcing reconstructions through improving the use of transfer functions.

Here, we investigate how the transfer function depends on eruption parameters and background conditions. Using simulations with the Earth system model CESM2-WACCM6, we explore a wide range of parameters, including eruption magnitude, latitude, plume composition, season, and plume height. By understanding the relationships between eruption parameters and resulting polar sulphate fluxes, we aim to improve the transfer function estimate used in the volcanic forcing for CMIP6 and shed light on the associated uncertainties.

How to cite: Krüger, K., Fuglestvedt, H., Zhuo, Z., and Burke, A.: Uncertainties of past volcanic forcing - Modelling the impacts of eruption parameters and atmospheric background conditions, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-20254, https://doi.org/10.5194/egusphere-egu24-20254, 2024.

Given the fact that many Icelandic volcanic systems are on the verge of an eruption, producing some of the largest volcanic eruptions over the past millennia, e.g., Öræfajökull, Bárðabunga, Grímsvötn and the Katla system, it is important to be able to predict potential changes in Northern Hemisphere (NH) climate variability in the following years after an NH eruption in due time. Recent volcanic activity in Iceland, e.g., Holuhraun 2014-2015 and Reykjanes/Geldingadalur 2021-2023, further demonstrates this urgency.

With the aim to contribute to improving the forcasting and adaptation strategies for the North Atlantic region we, as a first step, forced an Earth System Model (CESM1.2.2) with an idealized long-lasting high-latitude volcanic eruption to quantify i) the response within the stratospheric polar vortex and ii) the resulting response within the coupled climate system in the Northern Hemisphere (NH) by assessing the first 15 years following the eruption focusing on the winter (DJF) response. Here results will be presented showing evidence of sudden stratospheric warming events and a deceleration of the stratospheric polar vortex occurring in the second and third post-volcanic winter. This is identified in the temperature and zonal winds at 50hPa as a result of the large modelled surface cooling in the NH where Eliassen-Palmer wave flux calculations further support these findings. The strong stratospheric response identified further influences surface climate throughout the continental NH in the first 5 years following this event via the NAO. Our result suggest that two competing mechanisms are at work during these first years, partly explaining this long-lasting short-term response. The long-term impact is identified as a change in regional surface temperature and sea ice variability as well as a general strengthening of the AMOC, reaching a maximum in winter 2 and remaining positive throughout the run.

How to cite: Guðlaugsdóttir, H., Peings, Y., Zanchettin, D., and Magnúsdóttir, G.: Modelling the climate response following idealized long-lasting high latitude volcanic eruptions: The stratospheric response and resulting implications for North Atlantic surface weather, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-19839, https://doi.org/10.5194/egusphere-egu24-19839, 2024.

The reversibility of a wide range of components of the earth system was investigated by comparing forward and time-reversed 
historical and future simulations of a coupled earth system model known as the Beijing Normal University earth system 
model. Many characteristics of the climate system, including the surface temperature, ocean heat content (OHC), convective 
precipitation, total runof, ground evaporation, soil moisture, sea ice extent, and Atlantic Meridional Overturning Circulation, 
did not fully return to their initial values when the historical or future natural and anthropogenic forcing agents were reversed. 
The surface temperature and OHC declines lagged behind the decline in greenhouse gases (GHGs). Reverses in other variables occurred in direct response to the decline in GHGs. The sea level increased, even after all of the forces returned to the 
original values. Furthermore, most of the climate variables did not return to their original values because of thermal inertial. 
The end states of variables, other than those related to thermal storage, mainly depended on the original state of the natural 
and anthropogenic forces, and were unafected by the future growth rate of the GHGs. The climate policy implication of this 
study is that climate change cannot be completely reversed even if all the external forces are returned to their initial values

How to cite: Yang, S.: Reversibility of historical and future climate change with a complex earth system model, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-2964, https://doi.org/10.5194/egusphere-egu24-2964, 2024.

Despite the early warnings of the scientific community in general and of the IPCC in particular, we have entered decades in which climate models are no longer black boxes as the consequences of past emissions of greenhouse gases are emerging rapidly in multiple climate records. This unprecedented situation is likely to change our methods and our view of the respective roles of models and observations in understanding recent and predicting future climate change, regardless of the considered emission scenario. Among the key questions raised are the role of observations in model tuning versus projection constraining and the design of future model intercomparison projects. These questions will be illustrated by several recent studies aimed at constraining CMIP6 projections and, hopefully, with a fresh although critical look on the forthcoming CMIP7 project.

How to cite: Douville, H.: Confronting Earth System Model Trends with Observations: The Good, the Bad, and the Ugly, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-2903, https://doi.org/10.5194/egusphere-egu24-2903, 2024.

Water vapor is one of the fundamental atmospheric components, and as such, is one  Essential Climate Variable  (ECV) monitored by the Global Climate Observing System. In this work, the global water vapor Climate Data Record (CDR) generated within the ESA Water Vapor climate change initiative project (WV_cci) is used as reference (daily, 0.1°, 2003-2014) to evaluate a sample of the Coupled Model Intercomparison Project phase 6 (CMIP6) as well as the fifth generation ECMWF reanalysis (ERA5), with a focus on temporal signal decomposition. This temporal decomposition is performed using multi-resolution analysis (MRA). MRA is a mathematical tool which consists of decomposing a signal into its subcomponents on different time scales. Using this tool, the representation of the total column water vapor over the tropics in the CMIP6 models and ERA5 can be assessed separately from daily to annual and decadal time scales, including monthly and seasonal time scales. This approach is powerful for the  identification of  the relevant time scales for which CMIP6 predictions are most reliable. Hence, at the global-tropical scale, the MRA decomposition of the water vapor signal shows a good correlation between CMIP6 and WV_cci on both seasonal (2 - 8 months) and annual (1 - 1.4 year) time scales. Using a linear regression, we attempt to reconstruct the WV_cci signal using the CMIP6 models and ERA5 as explanatory variables based on the correlation found between the products and WV_cci at each level of decomposition. Such reconstruction highlights the scales of variability that are closest to the observed one. The presentation will detail the MRA approach and the most prominent results, as well as an extension to other parameters linked to atmospheric water vapor distribution, namely cloud cover and types and sea surface temperature. 

How to cite: Ngoungue Langue, C. G., Brogniez, H., and Naveau, P.: CMIP6 models evaluation using multi-resolution analysis and satellite observations : study of the atmospheric water vapor , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-10136, https://doi.org/10.5194/egusphere-egu24-10136, 2024.

Climate risk assessments must account for a wide range of possible future changes, so scientists often use many climate models in order to fully explore the range of potential changes in regional climates and their impacts. Many of the latest-generation global climate models have high values of effective climate sensitivity (EffCS), which are unlikely according to independent estimates of EffCS. It has been argued that these “hot” models are unrealistic and should therefore be excluded from analyses of climate change impacts. However, whether this would really improve regional impact assessments, or actually make them worse, is unclear. Here we show that there is no universal relationship between EffCS and projected changes in important climatic impact drivers. Analysing three different impacts - heavy rainfall, meteorological drought, and fire weather in important world regions, we find a significant correlation with EffCS only in some regions and for some metrics. Moreover, even in those cases, internal variability has a larger effect on projected changes than has EffCS. This means that impact studies should not select climate models based solely on their EffCS, which does not help constrain projections and may potentially neglect realistic impacts in models deemed “unrealistic” on the basis of their sensitivity. We recommend that model selection or filtering must be based on a more specific evaluation of models vis-à-vis the impact of interest.

How to cite: Swaminathan, R., Schewe, J., Walton, J., Zimmermann, K., Betts, R., Burton, C., Jones, C., Jones, C., Mengel, M., Reyer, C., Turner, A., and Weigel, K.: Regional impacts poorly constrained by climate sensitivity , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-5527, https://doi.org/10.5194/egusphere-egu24-5527, 2024.

Large uncertainty in model predictions of land carbon responses to climate change has been ubiquitously demonstrated in model intercomparison projects (MIPs). The large uncertainty become a major impediment in advancing climate change prediction. Thus, it is imperative to identify sources of the uncertainty before we can fully understand and address the uncertainty issue. In this presentation, I show a novel matrix approach to analytically pin down the sources of model uncertainty in predicting carbon dynamics in response to rising atmospheric CO₂ concentration and increasing temperature. We developed a matrix-based MIP by converting the carbon cycle module of eight land models (i.e., TEM, CENTURY4, DALEC2, TECO, FBDC, CASA, CLM5 and ORCHIDEE) into eight matrix models. In response to rising atmospheric CO₂ concentration and increasing temperature, predicted ecosystem net primary production (NPP), net ecosystem production (NEP), and net ecosystem carbon storage spread among the eight models as simulations go over time. We applied the traceability analysis method to decompose simulated carbon dynamics to their traceable components according to the matrix equations. Our analysis indicates that the uncertainty among the eight models was mainly due to inter-model difference in baseline carbon residence time and environmental scalar. Once the sources of model uncertainty were identified, we sequentially standardized model parameters to shrink simulated ecosystem carbon storage and NEP to almost none. Our study demonstrates that the sources of uncertainty in carbon cycle modeling can be precisely traced to model structures and parameters, regardless of their complexity, so that the uncertainty issue for MIPs can be precisely understood and well addressed.

How to cite: Luo, Y.: Uncertainty spreading and shrinking among eight land carbon cycle models in response to climate change, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-10567, https://doi.org/10.5194/egusphere-egu24-10567, 2024.

Questions regarding the uncertainty of trends in both historical and projected climate model simulations have been limited by uncertainty about the relative importance of internal variability and external forcing to trends over the relatively short observational record. For example, is the discrepancy between historically simulated tropical Pacific trends (El Niño-like) and observations (broadly, La Niña-like) over recent decades a reflection of sampling issues or model error in internal variability and/or forced global responses (either locally or remotely, such as from the Southern Ocean)? At the same time, it is known that systematic operational seasonal forecast errors (e.g., westward shift of ENSO) are dominated by model errors that develop quite quickly, on the order of a few months of forecast lead time.

Here, we suggest that climate model trend errors can be usefully investigated by examining their rapid development within seasonal hindcast datasets. We show that many apparent climate simulation trend discrepancies are evident in trends computed from monthly seasonal hindcasts over the 1994-2016 period for a suite of operational initialised forecast models from C3S and NMME, and in many cases are well developed even at short lead times. These hindcasts use models similar to CMIP-class models and include the same CMIP historical external forcings, but critically are initialised with observations, removing uncertainty related to internal variability. We find these trend errors in many different regions worldwide for several key variables, including sea surface temperature, precipitation and sea level pressure, and investigate their seasonal dependence as well. Notably, we find tropical Pacific "El Niño-like" SST trend errors in all seasons but spring, and related surface pressure, temperature, and precipitation errors in autumn and spring, especially in the North America region. We also find errors in Southern Ocean SSTs, which develop less rapidly than the tropical Pacific SST errors or their global teleconnections.

We suggest that these hindcast trend errors reflect sensitivity of the model mean biases to the changing radiative forcing, rather than a forced response. That is, similarity between errors in free running simulations and hindcasts is a result of the seasonal forecast models quickly transitioning from nature’s attractor to the climate model attractor, particularly in the atmospheric model component. This suggests that we might be able to better diagnose the climate model trend errors by looking at the early development of the forecast trend error in the seasonal forecast models.

How to cite: Beverley, J., Newman, M., and Hoell, A.: Rapid development of systematic trend errors in seasonal forecasts and their connection to CMIP6 trend errors, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-14596, https://doi.org/10.5194/egusphere-egu24-14596, 2024.

Tipping points are thresholds beyond which large, abrupt and possibly irreversible changes in the climate system or in large scale ecosystems would occur. The crossing of such tipping points under anthropogenic forcing poses a threat to biodiversity, food security, and human societies. However, due to the complexity of the processes involved, it remains notoriously difficult to determine exact thresholds that need to be avoided to stay within a “safe operating space” for humanity. Here, we map, for a variety of mitigation metrics, the crossing of thresholds, which we define to represent a wide range of deviations from the unperturbed state. We assess the crossing of these thresholds in a wide range of plausible future emission pathways: two climate mitigation scenarios (one with a strong overshoot) and one no-mitigation high-emissions scenario. These scenarios are simulated by the latest generation of Earth system models and by two Earth system models of intermediate complexity, for which we created large perturbed-parameter ensembles. Using this comprehensive model database we provide estimates of when and at which warming level 4 mitigation targets (thresholds) for 14 different impact metrics are exceeded along with an assessment of uncertainties. We find that under the high-emissions scenario, even the highest thresholds for many of the impact metrics are exceeded with high confidence, such as the expansion of ocean areas that are undersaturated with respect to aragonite, decreases in plankton biomass, Arctic summer sea ice extent, strength of the Atlantic meridional overturning circulation (AMOC), and subsurface oxygen concentration. The risk of exceeding a given mitigation target decreases under low-emissions and overshoot scenarios. Yet, exceedance of ambitious targets for aragonite undersaturation, Arctic summer sea ice extent, and steric sea level rise (SSLR) are projected to be difficult to avoid (high confidence) even under the low-emissions scenario. The overshoot scenario reduces the risk of exceeding mitigation targets related to Arctic summer sea ice extent, SSLR, AMOC and plankton biomass compared to the high-emissions scenario, particularly in the long-term. Uncertainties in Earth system model projections of net primary production prevent us from concluding on the risk of mitigation target exceedance for this impact metric.

How to cite: Bourgeois, T., Torres, O., Fröb, F., Jeltsch-Thömmes, A., Tran, G. T., Schwinger, J., Frölicher, T. L., Joos, F., Keller, D., Oschlies, A., and Bopp, L.: Pathways for avoiding ocean biogeochemical damage: Mitigation targets, mitigation options, and projections, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-5895, https://doi.org/10.5194/egusphere-egu24-5895, 2024.

Surface air-sea feedbacks play a pivotal role in modulating the amplitude of global ocean warming. Zhang and Li (2014, ZL14) introduced a simple theoretical framework to identify the driving processes responsible for the Sea Surface Temperature (SST) increase under global warming. This method involves decomposing changes in latent and upwelling longwave surface heat fluxes into two parts: one tied to direct atmospheric forcing and the other directly associated with local (SST) changes, termed feedback. Applying this heat budget equation across 53 CMIP5 and 6 models underscores the pivotal role of increased surface downwelling longwave radiation (DLR) in steering the amplitude of future global ocean warming. However, ZL14 solely considered DLR as a direct forcing, overlooking its substantial feedback response to surface warming.

In this study, we employ a novel methodology from Shakespeare and Roderick (2022, SR22) to decompose DLR changes into a direct radiative forcing and SST-related feedbacks, evaluating the implications of integrating the DLR feedback in the ZL14 framework. Our analysis is in line with SR22’s findings across 5 CMIP5 models, our results across 53 models indicate that roughly 90% of DLR increase emerges from feedbacks associated with the rising SST. The large ocean heat capacity transfers warming to the overlying atmosphere, increasing its DLR primarily through direct air temperature increase and the increasing greenhouse effect associated with increased water vapour.

Incorporating the DLR feedback in ZL14 framework yields a dominant effect of latent heat flux forcing on global ocean warming for both multi-model mean and intermodel diversity. This latent heat flux forcing is related to the evaporative cooling modulation associated with projected changes in the surface atmospheric circulation, and is highly correlated with the magnitude of the global average warming. This underscores the substantial influence of projected atmospheric circulation changes on the level of global average warming.

How to cite: Sadhvi, K., Lengaigne, M., Vialard, J., Danielli, V., Suresh, G., and Iyyappan, S.: A key role of surface atmospheric circulation changes in setting global ocean warming magnitude, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-10382, https://doi.org/10.5194/egusphere-egu24-10382, 2024.

Global climate change projections, such as those from the Coupled Model Intercomparison Project phase 6 (CMIP6), are still subject to substantial modelling uncertainties. A variety of Emergent Constraints (ECs) have been suggested to address these uncertainties, but remain heavily debated in the scientific community. Still, the central idea behind ECs to relate future projections to already observable quantities has no real substitute.

Here we discuss machine learning (ML) approaches for new types of controlling factor analyses (CFA) as a promising alternative. The principal idea is to use ML to find climate-invariant relationships in historical data, which also hold approximately under strong climate change scenarios. On the basis of existing big data archives such as those from the CMIPs, these climate-invariant relationships can be validated in perfect-climate-model frameworks.

From a ML perspective, we argue that CFA are promising for three reasons: (a) they can be objectively validated both for present-day data and future data and (b) they provide more direct - by design physically-plausible - links between historical observations and potential future climates compared to ECs and (c) they can take higher dimensional relationships into account that better characterize the still complex nature of large-scale emerging relationships. We highlight these advantages for three examples in the form of constraints on climate feedback mechanisms (clouds [1], stratospheric water vapour [2]) and forcings (aerosol-cloud interactions).

References:

1. Ceppi P. and Nowack P. Observational evidence that cloud feedback amplifies global warming, Proceedings of the National Academy of Sciences 118 (30), e2026290118 (2021). https://doi.org/10.1073/pnas.2026290118

2. Nowack P., Ceppi P., Davis S.M., Chiodo G., Ball W., Diallo M.A., Hassler B., Jia Y., Keeble J., and Joshi M. Response of stratospheric water vapour to warming constrained by satellite observations, Nature Geoscience 16, 577-583 (2023). https://doi.org/10.1038/s41561-023-01183-6

How to cite: Nowack, P. and Watson-Parris, D.: Why all emergent constraints are wrong but some are useful - a machine learning perspective, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-6750, https://doi.org/10.5194/egusphere-egu24-6750, 2024.

Precipitation and near-surface temperature from an ensemble of 36 new state‐of‐the‐art climate models under the Coupled Model Inter‐comparison Project phase 6 (CMIP6) are evaluated over Chile´s climate. The analysis is focused on four distinct climatic subregions: Northern Chile, Central Chile, Northern Patagonia, and Southern Patagonia. Over each of the subregions, first, we evaluate the performance of individual global climate models (GCMs) against a suit of precipitation and temperature observation-based gridded datasets over the historical period (1986-2014) and then we analyze the models’ projections for the end of the century (2080-2099) for four different shared socioeconomic pathways scenarios (SSP). Although the models are characterized by general wet and warm mean bias, they reproduce realistically the main spatiotemporal climatic variability over different subregions. However, none of the models is best across all subregions for both precipitation and temperature. Moreover, among the best performing models defined based on the Taylor skill score, one finds the so-called “hot models” likely exhibiting an overestimated climate sensitivity, which suggests caution in using these models for accessing future climate change in Chile. We found robust (90% of models agree in the direction of change) projected end-of-the-century reductions in mean annual precipitation for Central Chile (~-20% to ~-40%) and Northern Patagonia (~-10% to ~-30%) under scenario SSP585, but changes are strong from scenario SSP245 onwards, where precipitation is reduced by 10-20%. Northern Chile and Southern Patagonia show non-robust changes in precipitation across the models. Yet, future near-surface temperature warming presented high inter-model agreement across subregions, where the greatest increments occurred along the Andes Mountains. Northern Chile displays the strongest increment of up to ~6°C in SSP585, followed by Central Chile (up to ~5°C). Both Northern and Southern Patagonia show a corresponding increment by up to ~4°C. We also briefly discuss about the environmental and socio-economic implications of these future changes for Chile.

How to cite: Salazar, Á., Thatcher, M., Goubanova, K., Bernal, P., Guitérrez, J., and Squeo, F.: CMIP6 precipitation and temperature projections for Chile, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-111, https://doi.org/10.5194/egusphere-egu24-111, 2024.

Regional climate modelling has evolved significantly, offering versatile applications across various scales and resolutions. This study aims to provide a comprehensive framework for selecting top five Climate Models at each grid for climate variables in the Munneru River Basin, comes under Lower Krishna River Basin, India. Employing the NASA Earth Exchange Global Daily Downscaled Projections (NEX-GDDP) datasets, which are derived from General Circulation Model (GCM) runs under the Coupled Model Intercomparison Project Phase 6 (CMIP6), is compared with the observed precipitation, maximum, and minimum temperature datasets obtained from the Indian Meteorological Department (IMD). These datasets have a spatial resolution of (0.25° × 0.25°) and available from 1970 to 2014. The methodology adopted in this study uses advanced statistical techniques to evaluate the performance of the CMIP6 models. The study incorporates Multicriterion Decision-Making Techniques (MCDM) and Group Decision-Making (GDM) methodologies within the Reliable-Ensemble Averaging (REA) framework. MIROC-ES2L, GISS-E2-1-G and TaiESM1 are the top ranked models for precipitation data. Whereas, BCC-CSM2-MR, ACCESS-ESM1-5 and GFDL-CM4_gr2 obtained as most suitable RCMs for maximum temperature data. For minimum temperature data, MIROC-ES2L, KIOST-ESM and MIROC6 obtained as top ranked CMIP6 models. The projected climate variables, including precipitation, maximum temperature and minimum temperatures, under three distinct Shared Socioeconomic Pathways (SSP) scenarios: SSP 245, SSP 370 and SSP 585 extending up to the year 2100. The spatio-temporal analysis encompasses key climate parameters, identifying trends, variations, and potential anomalies in the Munneru River Basin. This study contributes to the broader context of regional climate modelling research and enhances our understanding of the Munneru River Basin's climate dynamics. The research findings presented in this study aim to understand the methodological advancements in regional climate modelling, performance assessments of CMIP6 models and the application of CMIP6 models in regional process studies.

How to cite: Keesara, V. R., Buri, E. S., and Kotapati Narayanaswamy, L.: Unveiling the Subjectivity in Ranking of NEX-GDDP-CMIP6 Climate Models Over Munneru River Basin, India, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-7159, https://doi.org/10.5194/egusphere-egu24-7159, 2024.