The pattern of surface warming plays a significant role in the Earth’s response to radiative forcing as it influences climate feedbacks. Distinct patterns of surface warming lead to divergent equilibrium and transient responses to identical forcing, emphasizing the need to analyse this pattern effect. While existing studies have primarily focused on assessing the influence of surface warming patterns on long-term warming (equilibrium climate sensitivity, committed warming), their role on the transient global warming remains poorly understood. Here, we introduce a novel analytical method to quantify the importance of evolving surface warming patterns on transient global warming. Our approach involves explicitly separating the radiative response caused by the global surface warming from the additional response induced by changing surface temperature patterns in the global energy budget. Using this new energy balance model, we assess the relative contribution of the radiative response induced by changing surface temperature patterns to global warming in idealized forcing experiments (1pctCO2) from 12 CMIP6 models. We show that the pattern effect consistently dampens global warming in 11 out of 12 model at decadal time scales. Specifically, we quantify that the transient climate response is reduced on average by 11% because of changing warming patterns. Our study demonstrates that distinct models exhibit significantly divergent transient global warming solely due to variations in the pattern effect. Overall, our results highlight the importance of changing warming patterns, through the pattern effect, in influencing decadal-scale transient warming. These findings support recent suggestions to incorporate warming pattern uncertainties in future climate projections.

The originality of this study is to propose a new calibration method based on two calibration phases between Jason-3 and Sentinel-6A (S6A) to better estimate the relative global and regional mean sea level drifts between the two missions. To date, a first calibration phase of approximately 12 months is planned from January 15, 2021, to December 31, 2021, when both satellites will be on the same orbit spaced out by approximately 30 seconds. This calibration will allow for a very accurate assessment of the GMSL bias between Jason-3 and S6A (less than 0.5 mm, see ​ Zawadzki and Ablain, 2016​). A second calibration phase after a few years would reduce the uncertainty levels of the GMSL (global mean seal level) drift estimate. The uncertainty would be low enough to detect any drift detrimental to the stability of the current GMSL record. It would indeed be possible to evaluate the stability between the two satellites with an accuracy at least 3 times better at the global scale than with the most accurate method to date. At regional scales, the second calibration phases would provide regional MSL drift estimates with very good precision. This study also shows that the time spent between the two calibration phases is significantly more sensitive than the length of the second calibration phase for the reduction in uncertainties. Finally, a possible scenario proposed by this study would consist of carrying out the beginning of the second calibration phase approximately 1.5-2 years after the first and for a duration of 3-4 months. This calibration would allow the detection of a relative GMSL drift of approximately 0.15 mm/yr and 0.4-0.5 mm/yr at oceanic basin scales (2000-4000 km).

The most simple representation of the dynamics of the global energy budget is the 0-dimensional energy balance model (EBM) introduced by Budyko (1965). Budyko’s EBM assumes a linear relationship between the Earth’s radiative response and the global surface temperature such that the dynamics of the global energy budget reads CdTs/dt = N = F - λ Ts, where Ts is the global surface temperature, N is the Earth Energy Imbalance, C is the ocean heat capacity and λ is the constant climate feedback parameter. Such simple conceptual model depicts reasonably well the centennial time scale response of the steady state preindustrial global energy budget under an anomalous forcing such as the increase of atmospheric greenhouse gases concentrations. For this reason it has served as the basis for the definition of the effective climate sensitivity to atmospheric CO2 concentrations. However recent studies identified limitations to Budyko’s EBM. Indeed climate model simulations show that the radiative response of the Earth not only depends on the global surface temperature but also on its geographical pattern: the so-called “pattern effect”. It arises from changes in the mix of radiative forcings, lag-dependent responses to forcings, or unforced variability and it leads to an apparent time variation in λ. This time variation must be accounted for in Budyko’s EBM to represent the longer term response of the global energy budget under increased CO2 concentrations. Here, a simple theory is developed to account for the time dependency of λ in the global energy budget. The resulting differential equation accurately reproduces the long term response (i.e. >200 years) of climate under abrupt changes in CO2 concentrations as simulated in the longrunmip experiment. Analysis of the asymptotic form of the differential equation yields a new expression of the climate sensitivity which not only depends on the climate feedback parameter but also on its temporal change. We evaluate this new climate sensitivity for all runs of the longrunmip experiment and show how it relates with the classical effective climate sensitivity from Gregory et al. (2004). We find that the spread in climate sensitivity among climate models of the longrunmip experiment is essentially due to different temporal changes in λ (and thus different pattern effect) among models.

Sea ice plays a crucial role on the ocean and is one of the most sensible indicators of climate change (IPCC 2013). The changing sea ice can be characterized by 4 Essential Climate Variables (ECVs) identified by the World Meteorological Global Organization Climate Observing System implementation plan (WMO Pub No.GCOS-200) : 1) sea ice extent, 2) sea ice concentration, 3) sea ice drift and 4) sea ice thickness. Since the 70ies, these 3 first variables are fairly well observed from space. However, before 2010 and the launch of CryoSat-2, sea ice thickness observations remain sparse and un- homogeneously distributed over time and space. In the Arctic ocean, we will show in this presentation that sea ice thickness time series can be extended to at least 16 years (2002-2017) using Envisat, CryoSat-2 and Sentinel-3 satellites with inter-missions biases calibrated and corrected. Among all potential inter-mission biases, the most impacting is certainly the transition from Low Resolution Mode (LRM) to Synthetic Aperture Radar (SAR) altimetry. This transition is particularly important over sea ice considering that the strong roughness heterogeneity, at basin scale and within the radar footprint, has different signatures in LRM and SAR modes. Here, we will focus on the transition between Envisat and CryoSat-2 common flight period (20010-2012). Results are validated against in situ observations (mooring, airborne, buoys and laser altimetry). This same methodology is applied to produce equivalent long term time series over Antarctica (2002-2017). In the southern ocean, the main difficulties rely on the severe lack of in-situ observations and on the ability to retrieve the snow depth. First results based on the Saral and Cryosat Ka-Ku frequency radar differences will be shown. Finally, we explain how this new global sea ice volume changes will provide new insights on the response to climate change. In particular, the final objective is to revisit the ocean freshwater budget and to provide a new constraint on the land ice melt contribution to sea level rise that is independent from the NASA Gravity Recovery and Climate Experiment measurements.