Snow cover response to temperature in observational and climate model ensembles

Transcription

1 Snow cover response to temperature in observational and climate model ensembles Lawrence Mudryk, Paul Kushner University of Toronto, Department of Physics Chris Derksen ECCC Climate Research Division Chad Thackeray University of Waterloo CESM Workshop CVCWG June 2016 Environment and Climate Change Canada Environnement et Changement climatique Canada

2 Motivation Evaluation of simulated snow cover extent (SCE) trends are complicated by three distinct sources of uncertainty. 1. Natural Variability 2. Model Uncertainty 3. Observational Uncertainty

3 Motivation Evaluation of simulated snow cover extent (SCE) trends are complicated by three distinct sources of uncertainty. 1. Natural Variability Large Initial Condition Ensembles 2. Model Uncertainty CMIP5 multi-model ensemble 3. Observational Uncertainty Observation-based ensembles

4 Three Types of Ensembles 1. Two Initial Condition Ensembles NCAR Large Ensemble (30 CESM1 realizations) CanSISE Large Ensemble (50 CanESM2 realizations) natural variability only within each ensemble 2. CMIP5 Multi-model Ensemble 24 models (53 realizations) with tas and snc archived for historical simulations (+ 5 years of rcp8.5) natural + model variability

5 Three Types of Ensembles 3. Observation-based TS Data (5) Global Instrument Records GISTEMP (GISS) HadCRUT4 (Had/CRU) NCDC (NOAA) Willmott and Matsuura (U Delaware) BEST (Berkley) SCE Data (7) Model/Reanalyses Mixed In situ Visible Satellite (B5 bolded) MERRA ERA-I-Land Crocus GLDAS2 GlobSnow (PM+in situ) Brown (model +in situ) NOAA CDR Substantial horizontal and vertical variation in snow properties mean that in situ observations from single locations rarely represent the larger scale mean.

6 The CanSISE Blended 5 SWE dataset Input: Canadian and international snow data Canadian research and know-how (NSERC CCAR, ECCC support) Output: internationally accessible value-added product

7 How consistent are observation-based estimates? (land > 30N) Temperature trends in good agreement with each other Six of seven SCE trends in good agreement with each other NOAA CDR trends disagree in Oct/Nov Spring time trends are stronger than other data sets NOAA CDR Individual Product Trend Outlier Bounds q1, (IQR)

8 How consistent are observation-based estimates? NOAA CDR is outlier in autumn over all regions/land types Overly strong spring time trends in NH trend stem from alpine regions midlatitudes and Arctic trends agree with other estimates NOAA CDR Individual Product Trend Outlier Bounds q1, (IQR)

9 How consistent are simulated TS/SCE Trends? (land > 30N) Observed trends lie within expected range of CMIP5 trends Strength of SCF trends primarily controlled by magnitude of temperature trends Observational Mean Model Ensemble Means IQR Outlier Bounds

10 SCE Trend Sensitivities (Loss Rate / Warming Rate) [x10 6 km 2 /decade] dsce/dt R 2 Spread among CMIP5 model SCE trends during spring principally controlled by temperature trend variability reasonably consistent SCE trend sensitivities CESM Realizations CanESM Realizations CMIP5 Realizations Ensemble Best-Fits

11 SCE Trend Sensitivities (Loss Rate / Warming Rate) [x10 6 km 2 /decade] dsce/dt R 2 observed range of SCE trend sensitivities are consistent with simulated values natural variability sufficient to explain spread of CMIP5 trends in the Arctic during spring CESM Realizations CanESM Realizations CMIP5 Realizations Ensemble Best-Fits Range of Obs Estimates (NOAA excluded)

12 SCE Trend Sensitivities (Loss Rate / Warming Rate) [x10 6 km 2 /decade] dsce/dt R 2 at a minimum natural variability is responsible for ~2/3 of total spread CESM Realizations CanESM Realizations CMIP5 Realizations CMIP5 Model Means Ensemble Best-Fits Range of Obs Estimates (NOAA excluded)

13 Midlatitudes (ONDJAMA) R2 temperature trends explain less SCE trend variability in the Arctic during fall Sensitivities are higher in midlatitudes than in Arctic or alpine regions Simulated midlatitude and alpine sensitivities underestimate SCE loss per degree warming dsce/dt Model differences required to explain CMIP5 spread outside of Arctic springtime Arctic (SON) [x106 km2/decade] R2 R2 R2 dsce/dt Alpine (AM) dsce/dt dsce/dt

14 Summary of SCE Trends Multiple observation-based estimates of snow cover trends are important to use whenever possible and lead to improved comparison with simulated trends Observation-based and modelled SCE trends appear to be principally controlled by temperature trends Observed midlatitude and alpine snow cover loss is stronger than simulated; Arctic snow cover loss is well modelled Natural variability sufficient to explain spread of CMIP5 trends in the Arctic during spring Other regions and seasons require model differences to explain the CMIP5 model spread Thank You!

15 Model vs Natural Variability Spread due to Natural Variability Full CMIP5 Spread Arctic Midlatitudes

16 Model vs Natural Variability Spread due to Natural Variability Full CMIP5 Spread Arctic Midlatitudes

17 Midlatitudes U Toronto Realizations (ONDJAMA) R2 dsce/dt Arctic (SON) [x106 km2/decade] R2 R2 R2 dsce/dt Alpine (AM) dsce/dt dsce/dt

18 Modelled Temperature and SCE Trends Strength of global SCF trends primarily controlled by magnitude of global temperature trends correlations between TS and SCE trends are higher in CESM and CMIP5 ensembles than CanESM ensemble CanESM CESM CMIP5

19 SCE TS SLP

20 SE Trends: Threshold Sensitivity ONDJFMA trend range for reasonable threshold values trends show consistently negative trends and similar seasonality inter-data set spread > uncertainty due to threshold selection

21 Direct SCE vs SWE-derived SCE agreement between direct model SCE and SWE-derived SCE are resolution and threshold dependent. however the resolution of the 5 SWE products is likely fine enough to expect reasonable agreement (explicitly confirmed for MERRA data)

22 Observational SCE Trends and Snowfall Trend Estimates NOAA SCE trends are difficult to reconcile with local climatological temperatures and snowfall estimates GlobSnow and Brown estimates (merged with in situ data) are in better agreement with local temperature and snowfall estimates Stippling: MERRA snowfall < 0 Shading: SCE Trends

23 Observational SCE Trends and Snowfall Trend Estimates similar conclusions over North America Stippling: MERRA snowfall < 0 Shading: SCE Trends

24 Motivation Previous work showed some general agreement between simulated snow cover trends and observed values but poor seasonality