Climate and cryosphere: What longterm obervations do modelers need? G. Krinner, LGGE/CNRS Grenoble

Climate and cryosphere: What longterm obervations do modelers need? G. Krinner, LGGE/CNRS Grenoble

Polar regions: High climate variability Projected timing of climate departure from recent variability, RCP8.5 (Mora et al., 2013) Consequence: Even longer time series required to assess means, trends, variability

Cryosphere: Usually not where all the people are Relatively few in situ datasets But global impacts of melting ice: Sea level Permafrost carbon feedback Freshwater availability

Cryosphere: Where even satellites do not always go Poles often a blind spot for satellites Source for uncertainty in global temperature reconstructions, and for model evaluation

Long-term observations of the cryosphere: What use for models Modelers LOVE gridded, global, long-term data sets (satellites, reanalyses), for obvious reasons Need for long-term in situ observations: Process understanding and parameterization: Ice sheets: Basal processes Stable boundary layer processes Land surface processes: Permafrost, snow Model evaluation In the following: A few thoughts on long-term cryosphere observations from a modeler s perspective, for the different components of the cryosphere

Permafrost Potential permafrost carbon feedback But no permafrost carbon reservoir in CMIP5 models (Ciais et al., 2013)

Permafrost is hard to see from space! Current state of the permafrost carbon system unclear Increased need for site observations: For model development and evaluation For assessing the state of the system Requirements: Representativity Broad spatial coverage (transects) Meteorological forcing data Good dialogue, process knowledge transfer Ideally co-design of observational strategies (Hugelius et al., 2013, 2014)

Snow Snow extent data sets OK, models compare well on average, strong trend underestimated Many snow observation series interrupted (Russia) (Derksen and Brown, 2012) (Brutel-Vuilmet et al., 2013)

Snow insulation Substantial problems, impacting e.g. simulated permafrost extent (Koven et al., 2013)

ESA SnowPEX Dataset intercomparison ESM-SnowMIP Snow module intercomparison Long-term site observations: span different types of climate regimes Global long-term datasets

Sea ice No long-term site observations Sea-ice extent and area: Very prominent observable Progress in climate models Possibly due to some improvements of parameterisations (e.g. melt ponds), probably due to improved atmosphere and ocean (Flato et al., 2013)

Beyond sea-ice extent Sea-ice thickness: Data sets of lesser quality, problems in summer (melt ponds) and winter (snow) Strong focus on the Arctic Snow on sea ice: Big forgotten issue

Mountain Glaciers Mountain glaciers: A heterogeneous environment! GlacierMIP Need for representative long-term series (extremely precious). Few observations in Asia, South America Satellite observations of mass balance: low time resolution (Zemp et al., 2015)

Ice sheets Reconciled (almost) satellite-based mass balance estimates Need for complementary long-term in-situ mass balance observations, for control. Spatial coverage: coastal areas (but not only) Transects Terms of surface mass balance pertinent for climate modelers. Major challenges Accumulation Surface melt Blowing snow (Hanna et al., 2013)

Recent modeling progress Emergence of a new generation of ice sheet models : Increased resolution Full-Stokes resolution of the flow Inversion methods (basal drag, viscosity) (Gillet-Chaulet et al., 2012) Coupled modeling in ESMs: ISMIP6 Long-term site observations required for: A better understanding of melt-induced acceleration and other ice sheet-bedrock interactions A better understanding of ice sheet-ocean interactions

Summary Long-term site observations: For model development and evaluation Need for (gap-filled) ancillary data: meteorological forcing Need representative sites For snow and permafrost, but also ice sheets (e.g. blowing snow) Gridded large-scale data: Main challenges Seasonal snow: mass, not only extent Permafrost: not observable from space Sea ice: Go beyond sea-ice extent Ice sheet mass balance: Convergence between data sets. Surface mass balance terms (e.g. precipitation)