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Observational needs for seasonal to decadal forecasts
Roger Saunders Met Office
Acknowledgements to Adam Scaife and Doug Smith
ENSO forecasting
2015/16
Nino 3.4
Ensemble prediction systems using a coupled ocean-atmosphere model generate probabilistic forecasts up to six months ahead.
ORCA ¼° ORCA 1°
Higher ocean resolution is important
2 years of monthly mean temperature
at 370m depth
North Atlantic Oscillation The North Atlantic Oscillation (NAO) is a climatic phenomenon in the North Atlantic Ocean of fluctuations in the difference of atmospheric pressure at sea level between the Icelandic low and the Azores high. Many things trigger the NAO.
NAO in Winter now predictable
Sudden stratospheric warming (SSW) and strong polar vortex (SPV) events
SPV
SSW
Observations Ensemble mean
DJF hindcasts started from 1 Nov
Seasonal winter forecasts and the stratosphere
Atmospheric Science Letters 25 OCT 2015 DOI: 10.1002/asl.598
http://onlinelibrary.wiley.com/doi/10.1002/asl.598/full#asl2598-fig-0004
The stratospheric state, which is initialised using satellite data, is crucial to the predictability of the NAO
Atmospheric initial conditions and the predictability of the Arctic Oscillation AO
Stockdale et al 2015
Geophysical Research Letters Volume 42, Issue 4, pages 1173-1179, 26 FEB 2015 DOI: 10.1002/2014GL062681
http://onlinelibrary.wiley.com/doi/10.1002/2014GL062681/full#grl52611-fig-0002
Control uses the correct initial conditions. SHIFT uses only atmosphere initial conditions from the correct date; ocean, sea ice, and land surface are taken from the preceding year. The error bars show the 1 standard deviation uncertainty of the ensemble mean.
Forecasts of the AO from differing initial conditions
Atmospheric initial conditions dominate predictability not the surface
Trends in solar spectral irradiance variability in the visible and near infrared
Geophysical Research Letters Volume 36, Issue 7, L07801, 1 APR 2009 DOI: 10.1029/2008GL036797
http://onlinelibrary.wiley.com/doi/10.1029/2008GL036797/full#grl25584-fig-0001
The SIM spectral irradiance data integrated into discrete bands.
Spectral irradiance changes with decreasing solar activity
UV IR
Solar Models not in agreement over solar irradiance distribution
Observations
We need better space based estimates of incoming solar SPECTRAL irradiance to pin down the UV component (200-300nm) of solar variability
Last Winter 2014/15
© Crown copyright Met Office
From October From November Observations
Very clear signals for a westerly winter from October Good agreement with subsequent observations Rossby wave emanating from the tropical Atlantic
Initialisation for seasonal forecasts • Ocean and atmosphere analyses are
currently the main source of initialisation • SST from assimilation in ocean model • Hence satellite data are only indirectly used • Moving to coupled DA and models • The key variables for seasonal forecasts
are: • Sub-surface ocean temperatures • SST, Salinity, Sea-Ice (cover & thickness) • Stratospheric state • Solar spectral irradiance, Soil moisture, Snow cover
© Crown copyright Met Office (Smith et al. 2010)
Impact of initialization on 5 year mean temperature skill
Initialised - Uninitialised Skill of initialised predictions
• Skilful almost everywhere (positive correlations)
• Mostly due to external forcing
• Initialisation gives improved skill mainly in North Atlantic and tropical Pacific
Correlations
Physical basis for improved skill
Robson et al 2012, Yeager et al 2012; also Robson et al 2014, Müller et al 2014 for 1960s cooling
Atlantic sub-polar gyre 500m temp
• Rapid warming of Atlantic sub-polar gyre in mid 1990s • Initialization improves predictions • Increased northward heat transport • Due to ocean dynamics (increased Atlantic overturning circulation)
Observations Initialised (DePreSys) Uninitialised (NoAssim)
Hea
t Con
tent
© Crown copyright Met Office
Predicted cooling of North Atlantic
(Hermanson et al, 2014)
• Atlantic predicted to cool in response to weakening of ocean overturning • Likely to cause climate impacts around the Atlantic basin • Not a reversal, but impacts associated with warm Atlantic less likely: cold winters and wet summers in Europe less likely fewer hurricanes than recent peaks reduced Sahel rainfall reduced risk of drought in SW USA
Temperature Ocean circulation
Atlantic tropical storms
Initialisation for decadal forecasts • Ocean and Atmosphere Reanalyses are
the main source of initialisation and validation of hindcasts
• Upper ocean and SST (HadISST) used • The key variables for decadal forecasts
are: • Upper ocean temperatures and salinity (~500m) • Ocean currents (e.g. RAPID array) • Stratospheric state QBO • Sea-ice thickness • Solar spectral irradiance • Soil moisture? Snow Cover?
Implications for Observing System 1. Note satellite data assimilation for coupled global ocean
and atmosphere models underpins seasonal and decadal prediction
2. SST and deeper layer ocean temperatures and salinity essential
3. Ocean basin circulation important currents
4. Sea-ice thickness and snow observations should be continued and improved relative to the current operational satellites (e.g. treatment of melt ponds)
5. Continue and enhance? observations of stratospheric state limb view measurements?
6. Initiate long term measurements of spectral solar irradiance
7. Maintain and improve soil moisture measurements
Data Provision 1. Observations and analyses required < few days delay
of real time for seasonal forecasts
2. Observations and analyses required < 1 month of real time for decadal forecasts
3. Global coverage of observations at <1 deg resolution for ocean for large scale anomalies but model needs to be at 0.25 deg for ocean circulation.
4. Daily sampling
5. The solar irradiance is not required close to real time as we just need the amplitude of the 11year solar cycle
6. “Observations” • ARGO, RAPID • HadISST • ERA (Ocean/Atmosphere) • GLOSEA
Satellite Observational capabilities GCOS ECV 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 Sensors
Atmospheric Surface precip SSMIS, AMSR, MWRI, TRMM, GPM, ATMS, GEO Vis/IR Surface wind ASCAT, OSCAT, HY-2, RapidScat, WindRAD TOA radn budget CERES, EarthCARE, SCARAB, RBI Solar irradiance TSIS, ACRIM, SORCE, Picard Temp profile Sounder radiances, GPS-RO Water vapour profile Sounder radiances, GPS-ZTD, [Column SSMIS, OLCI] Wind profile AMVs, ADM
Cloud properties Cloudsat, EarthCare, VIS/IR imagers (GEO/LEO) Carbon dioxide AIRS, IASI,OCO-2/3,CRIS, GOSAT, GAS Methane AIRS, IASI, GOSAT, CrIS, MTG-IRS, Schiamachy, MOPPIT Ozone GOME-2,IASI,AIRS,CRIS, IR, UV limb, OMPS, OMI Other GHG IASI, GOME-2, UV/IR limb, GOSAT, Sentinel-5 Aerosols AVHRR, VIIRS, GOME-2, MERIS, MODIS, Sent-4/5, MTG
Oceanic SST AATSR, SLSTR, AVHRR, AMSR-2, MODIS, VIIRS, GeoIR Surface salinity SMOS, Aquarius, SMAP Sea level TOPEX,Jason-1,2,3, Sentinel-3 ALT, Sentinel-6 Sea state Jason-1,2 Sentinel 3 ALT
Sea-ice SSM/I, AMSR, SSMI(S) [Thickness Cryosat-2, ICESAT-2, SMOS] Currents Jason-1,2,3?, Sentinel-3 ALT Ocean colour MERIS, MODIS, VIIRS, OLCI
Terrestrial LST AATSR, SLSTR, AVHRR, AMSR, MODIS, VIIRS, CrIS, IASI Lake levels Jason-1,2,3, Sentinel 3 ALT Snow cover and SWE SSMIS, AMSR, AVHRR, MODIS, Geo Imagers Glaciers and ice caps GRACE, Cryosat-2, ICESat, ASTER, Landsat Permafrost MODIS, VIRSS,SAR Albedo AVHRR, MODIS, VIRSS Land cover (inc veg) Sentinel-2, MODIS, VIRSS, Landsat, TerraSAR fAPAR MODIS, VIRSS, MERIS, Sentinel-2 LAI MODIS, VIRSS, MERIS, Sentinel-2 Biomass Sentinel-1 SAR, BIOMASS Fire Geo imagers, ATSR, AVHRR, VIIRS, Sentinel-3 Soil moisture ASCAT, SMOS, SMAP Ground water GRACE, GRACE-2
Key Good capability Some capability but needs improvement Poor capability Capability lost No capability
Loss of limb view stratospheric measurements
Recommendations
1. Ensure continuity and improvement of following satellite measurements:
– Ocean salinity and temperatures
– Sea-Ice thickness
– Stratospheric temperature and winds
– Solar spectral irradiance
– Soil moisture? Snow?
2. Develop capability to measure ocean currents