Climate Change: Moonshine, Millions of Models, & Billions of Data New Ways to Sort Fact from Fiction

Transcription

1 Climate Change: Moonshine, Millions of Models, & Billions of Data New Ways to Sort Fact from Fiction Bruce Wielicki NASA Langley Research Center June 5, 2008 VIMS Lecture

2 Outline Is that earthshine or moonshine? Fact vs fiction in climate change? How would you know an accurate climate model if you met one?

3 Climate System Energy Balance: Kiehl & Trenberth 1997

4 IPCC 2007 Climate Science Report Cloud Feedback remains the largest climate sensitivity uncertainty. Factor of 3 in sensitivity by Low clouds are the dominant problem. For low clouds, albedo change dominates cloud feedback Aerosol Indirect Effect on cloud remains the largest radiative forcing uncertainty. Factor of 3 in current radiative forcing. Climate model predictions for global temperature change don't diverge until after 2040 to 2050 between high and low sensitivity climate systems. but large temperature change by 2100 between low/high sensitivity why? more sensitive models store heat in the ocean more rapidly and their response is more delayed than less sensitive models need to determine cloud feedback before temperature shows what the climate sensitivity is (2040 to 2050 is too late).

5 Amount of change for a factor of 6 in climate model sensitivity (2K to 12K for doubling CO 2 ) Dynamics variables not very sensitive Cloud, Radiation, Sea Ice variables very sensitive Weather = dynamics, Climate = energetics Need Climate Change OSSEs, Climate Obs. Reqmts Murphy et al. Nature, 2004

6 Global Dimming: Wild et al., 2004 GRL "Several studies indicate that incident shortwave radiation at land surfaces has significantly decreased between 1960 and Despite this, land temperature has increased by over 0.4C over the same period."

7 Earthshine: Palle et al., Science, May 2004 Global Albedo Anomaly SW Reflected Flux Anomaly "These large variations in reflectance imply climatologically significant cloud-driven changes in Earth's radiation budget, which is consistent with the large tropospheric warming that has occurred over the most recent decades."

8 How large are these SW Flux Changes? Solar constant = 1365 Wm -2 Global average TOA insolation = 1365/4 = 341 Wm -2 Global average Reflected SW Flux ~ 100 Wm -2, Albedo ~ 30% Global SW atmosphere absorption ~ 20%, surface insolation ~ 50% Global net energy balance ~ 341 (Insolation) = 100 (SW) (LW) Anthropogenic Radiative Forcing (IPCC) ~ 0.6 Wm -2 per decade A low cloud feedback of 50% (SW dominated) ~ 0.3 Wm -2 per decade. This is a change of 0.3% relative or 1/20th the earthshine/dimming changes. 6 Wm -2 Changes are HUGE: factor of 10 larger than anthropogenic forcing. Climate skeptics breathe a sigh of relief: global warming is lost in the noise. Can natural variability be this large? Fact or Fiction?

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10 0.21 Wm -2 Shows consistent calibration stability at < 0.3 Wm -2 per decade (95% conf) Unfortunately only works for tropical mean ocean (nband vs bband issues) Regional trends differ by +2 to -5 Wm -2 /decade SeaWiFS vs CERES Loeb et al J. Climate

11 CERES Shortwave TOA Reflected Flux Changes: Ties to Changing Cloud Fraction Tropics drive global albedo variations: global is in phase with tropics and 1/2 the magnitude Cloud fraction variations are the cause (not optical depth) Unscrambling climate signal cause and effect requires complete parameter set at climate accuracy, e.g. for forcing/response energetics: radiation, aerosol, cloud, land, snow/ice, temperature, humidity, precipitation

12 Using CERES to Determine Length of Climate Data Record Needed to Constrain Cloud Feedback Half of Anthrop Forcing of 0.6 Wm -2 /decade Given climate variability, 15 to 20 years is required to first detect climate trends at cloud feedback level with 90% confidence, and 18 to 25 years to constrain to +/- 25% in climate sensitivity Loeb et al J. Climate

13 How well can we pull climate records from meteorological satellite data like ISCCP from geostationary? Geo calibration & sampling errors dominate interannual signals Uncertainty in Geo trends are a factor of 10 larger than climate goal: can we learn how to improve past data sets? Loeb et al., 2007 J. Climate

14 Annual Mean Global SW TOA Flux Anomaly (Earthshine versus CERES: 2000 to 2004) Earthshine data implies large change of 6 Wm -2 in global reflected SW flux: is the Earth's albedo changing? (Palle et al., Science, 2004) CERES shows an order of magnitude less variability than Earthshine Earthshine approach is incapable of capturing changes in global albedo at climate accuracy. Loeb et al GRL

15 "Global Dimming": is it real? What about new CERES fusion satellite surface fluxes? ARM/BSRN/CMDL/Surfrad Surface Radiation Sites

16 Surface SW Flux Validation Noise Spatial mismatch of surface point to satellite area Error decreases as simple 1/sqrt(N) random noise: but takes 20 sites for 1 year to reach 1 Wm -2 : 10,000 samples. (Wielicki, AMS Radiation Conference, 2006)

17 CERES Surface Fluxes vs Surface Sites: Interannual Anomalies Consistent at 0.2% or 0.3 Wm -2 Global satellite sampling of radiation fields remains key: regional variability (climate noise) is very large: 10 times the global forcing of 0.6 Wm -2 /decade: even averaging 40 disperse surface sites. Result from GEWEX Radiative Flux Assessment (in progress)

18 Early Cloud Feedback Signals in the Arctic from CERES data Seiji Kato and the CERES Science Team Mean Cloud Fraction at Barrow AK Trends derived from Terra and Aqua Data over the Arctic Terra Aqua Linear Fit to Terra Missing days CERES: Derived from MODIS radiances by the CERES cloud algorithm. Radar: Derived from a ARM cloud radar. Lasers: Derived from a micro-pulse lidar and a Vaisala ceilometer Error bars and dashed lines indicate max. and min. during 4 years. Snow/sea ice fraction changed at a rate of per decade (significant at an 80% confidence level) Cloud fraction changed at a rate of per decade (significant at an 80% confidence level) Albedo change is insignificant at an 80% confidence level. From Kato, S., N. G. Loeb, P. Minnis, J. A. Francis, T. P. Charlock, D. A. Rutan, E. E. Clothiaux, S. Sun-Mack, 2006: Seasonal and Interannual Variations of Top-of-Atmosphere Irradiance and Cloud Cover over Polar Regions Derived from the CERES Data Set, Geophys. Res. Lett., 33, L19804, doi: /2006gl

19 Ocean Heat Content and Net Radiation changes in ocean heat content should = radiative flux anomalies Ocean Cooling? Lyman et al., Science 2006 Net Radiation: no Altimeter Sea Level: no GRACE Ice Sheet: no 1992 to 2003 data from Wong et al. J. Climate 2006 Possible Causes of 2004/5 Drop in Ocean Heat Storage: - transition from XBT to ARGO ocean in-situ data and sampling? - cooling upper 750m of the ocean, but larger heating deeper? - unmeasured heating under sea ice? - The answer: warm bias in XBTs (dominate pre-2002) cold bias in ARGO (dominate post 2002): no cooling in 2004/5 when bias is corrected. no mystery.

20 Climate Models: Uncertainty, Observations, and OSSEs Model Runs provided by ClimatePrediction.Net research group at Oxford Univ., Myles Allen, David Stainforth Model Run Analysis: Yong Hu and B. Wielicki Ongoing discussions/debates with: Dave Randall, Graeme Stephens, Tom Ackerman, Bill Collins, Kevin Trenberth, Leo Donner, Christian Jacob, Bill Rossow, Phil Rasche, Don Anderson, Max Suarez

21 Summer 2005 Gordon Conference on Climate What data do climate models need? When are climate models accurate enough? What are the climate observing system requirements? variables? time scales? spatial scales? priority for cost/benefit? How do we relate model & observation improvements to narrowing uncertainty in climate prediction? Why can t we get resources to attack the tough problems?

22 Climate OSSEs How do we determine decadal climate prediction accuracy? IPCC uses differences in model predictions: basically a preponderance of circumstantial evidence. IPCC AR4 draft admits no current way to relate errors in prediction of past climate to uncertainty in future prediction. No current way to relate model improvements in predicting past climate states (e.g ) to prediction uncertainty This is not the case for weather prediction: why? 1000s of days of weather, but not 1000s of decades of climate to test. How do we determine climate observing system reqmts? For global decadal change we can use anthropogenic forcing, response, feedbacks: e.g. Ohring et al., 2005 BAMS No known method for ANY other time/space scales: such as regional, zonal, seasonal, diurnal, etc: critical to design obs This is not the case for weather prediction: OSSEs exist How can we develop physically rigorous climate prediction OSSEs?

23 Stainforth et al., 2005, Nature

24 Amount of change for a factor of 6 in climate model sensitivity, by climate variable: clouds dominate Murphy et al., Nature, 2004

25 How do we relate model errors versus observations to climate prediction uncertainty? PPE Planet I vs Planet J is a simulation of Climate Model vs Earth Each Planet is a different Earth-like physical climate system Planets have physics which differ in processes we don t understand Can we use these Planet s past climate differences to predict differences in their future climate? This is in fact the same as asking if we can use past climate differences in Earth vs Climate Model to predict uncertainty in predicting the future climate of Earth. If this is not successful: we cannot predict uncertainty in predicting the real Earth: until after the fact.

26 Neural Net Structure Input Variables Planet I - Planet J base state CO 2 climate TOA SW Flux TOA LW Flux Total Cloud Fraction Conv. Cloud Fraction Total Precipitation Large Scale Snowfall Large Scale Rainfall Surface Latent Ht Flux Surface Net SW Flux Surface Net LW Flux Surface Net Radiation Neural Network Output Variables Planet I - Planet J 2xCO 2 minus 1xCO 2 Surface Temperature Summer U.S. Precip Sea Level etc...

27 Neural Net Prediction of Climate Sensitivity Planet I minus Planet J Doubled CO 2 Global Temp Change 95% confidence bound of +/- 0.8C 33 climate model variables Neural Net Prediction: Doubled CO 2 Global Temp Change (uses Planet I and J normal CO 2 climate only) Y. Hu, B. Wielicki, M. Allen

28 Linear Regression Prediction of Climate Sensitivity Planet I minus Planet J Doubled CO 2 Global Temp Change 95% confidence bound of 95% +/- 0.8C confidence bound of +/- 2.0C 33 climate model variables Neural Net Prediction: Doubled CO 2 Global Temp Change (uses Planet I and J normal CO 2 climate only!) Y. Hu, B. Wielicki, M. Allen

29 Neural Net Results vs. No. of Variables Doubling CO 2 Global Temperature Uncertainty (1s) 33 variables 0.41K 11 variables 0.66K 4 variables 0.89K Four variables with largest constraint on climate sensitivity Top of atmosphere shortwave reflected flux Total cloud fraction Convective cloud fraction Total precipitation Neural net roughly 2.5 times more accurate than multiple linear regression Y. Hu, B. Wielicki, M. Allen

30 Neural Net Structure Climate OSSEs Input Variables Planet I - Planet J base state CO 2 climate Add Observation Error Output Variables Planet I - Planet J 2xCO 2 minus 1xCO 2 TOA SW Flux TOA LW Flux Total Cloud Fraction Conv. Cloud Fraction Total Precipitation Large Scale Snowfall Large Scale Rainfall Surface Latent Ht Flux Surface Net SW Flux Surface Net LW Flux Surface Net Radiation Bias, σ Bias, σ Bias, σ Bias, σ Bias, σ Bias, σ Bias, σ Bias, σ Bias, σ Bias, σ Bias, σ Neural Network Surface Temperature Summer U.S. Precip Sea Level etc... Difference in neural net performance with and without observation errors Isolates effect of observation error on constraining climate uncertainty

31 Effect of Observation Error on Neural Net Prediction Accuracy (2xCO 2, Deg C) If no observation constraint: sigma 1.5 K

32 Effect of Observation Error on Neural Net Prediction Accuracy (2xCO 2, Deg C) Base State Climate Differences to Predict (no decadal change information used) (error specified as % of mean 2xCO 2 change for any variable) Error as Percentage of Mean Doubled CO2 Change 0% 10% 25% 50% 100% Variables

33 Effect of Observation Error on Neural Net Prediction Accuracy (2xCO 2, Deg C) Climate Change Differences to Predict (no base state information used) Avoid all variables trivially related to Ts (e.g. PW) (error specified as % of mean 2xCO 2 change for any variable) Error as Percentage of Mean Doubled CO2 Change 0% 10% 30% 50% 100% Variables

34 Effect of Observation Error on Neural Net Prediction Accuracy (2xCO 2, Deg C) Climate Change Differences to Predict (no base state information used) Avoid all variables trivially related to Ts (e.g. PW) Test CPDN trained Neural Net using IPCC Runs (error specified as % of mean 2xCO 2 change for any variable) Error as Percentage of Mean Doubled CO2 Change 0% 10% 30% 50% 100% Variables Variables: TOA SW All-sky&Clear, TOA LW, Precip, Snowfall, Sfc SW Down

35 Next Steps: More complete metrics than simple global means e.g. Manabe seasonal cloud forcing e.g. recent Seasonal Temperature metrics: early results indicate they don't work for PPE set. Improve neural net accuracy with array gate computer Greatly increases complexity allowed (# of metrics) Use more realistic CPDN simulations: Coupled Ocean/Atmosphere (now 1000 runs) Realistic time varying forcings Realistic decadal change Realistic decadal climate noise

36 Another View of Climate OSSEs Respond to the papers in Science, October 2007 Why Is Climate Sensitivity So Unpredictable? by Gerald Roe and Marcia Baker Call Off the Quest by Myles Allen and David J. Frame Demonstrate that the framework presented in this paper can be used to define accuracy measurement requirements for cloud and other feedbacks and for measurements like: CERES (Clouds and the Earth s Radiant Energy System) instruments measure broadband radiation with accuracy to detect change in broadband solar, infrared and net radiative energy globally: including the effect of clouds on radiation. CLARREO is a future mission which will allow climate change accuracy calibration of a wide range of solar reflected and thermal emitted satellite sensors ( NIST in orbit ) 2

37 CLARREO / CERES Science Connections: Climate Sensitivity Uncertainty The Climate Feedback System Reducing uncertainty in predictions of ΔT is critical for public policy since changes in global surface temperature drive changes in sea level and precipitation 3

38 CLARREO / CERES Science Connections: Climate Sensitivity Uncertainty Uncertainty in Feedback Defines Climate Sensitivity Uncertainty The skewed tail of high climate sensitivity is inevitable in a feedback system IPCC Mean Sensitivity 2σ Feedback Factor, f 4

39 CLARREO / CERES Science Connections: Climate Sensitivity Uncertainty IPCC Climate Feedback Uncertainty Feedback Factor Uncertainty (2 s) Current IPCC (Soden & Held 2006) Total Cloud W. Vapor Lapse Rate Surface Albedo The uncertainty in climate feedback is driven by these three components. The feedback for the climate system is f = 0.62 ± 0.26 (2σ) 5

40 CLARREO / CERES Science Connections: Climate Sensitivity Uncertainty Current Climate Uncertainty ΔT for 2 x CO 2 ( o C) 0.26 Feedback Factor, f Current measured feedback uncertainties result in large uncertainties in predicted ΔT (Roe and Baker, 2007). ΔTo = the Earth s temperature as a simple blackbody 6

41 CLARREO Requirements Based on Reducing Feedback Uncertainty: Cloud Feedback Example Reducing Climate Uncertainty Requires a More Accurate Measurement of Feedback ΔT for 2 x CO 2 ( o C) 0.09 Feedback Factor, f High accuracy measurements of climate change can constrain predictions of ΔT through improved estimates of the feedback. The accuracy requirement is driven by the goal for climate uncertainty reduction. 7

42 CLARREO Requirements Based on Reducing Feedback Uncertainty: Cloud Feedback Example CLARREO Reduces Climate Uncertainty Feedback Factor Uncertainty (2 s) Current IPCC (Soden & Held 2006) With CLARREO Total Cloud W. Vapor Lapse Rate Surface Albedo These feedback uncertainty goals define the climate change observation requirements 8

43 CLARREO Requirements Based on Reducing Feedback Uncertainty: Cloud Feedback Example Cloud Feedback Uncertainty Goal Defines the Observation Requirement 9

44 CLARREO Requirements Based on Reducing Feedback Uncertainty: Cloud Feedback Example Decadal Trend Observation Requirement The uncertainty goal for feedback factor f sets the observation goal for Net Cloud Radiative Forcing (CRF) at 1.2 Wm -2 /K IPCC models predict a 0.2 K / decade warming in the next few decades independent of sensitivity. (because the warming is controlled by the slow ocean response time) Therefore, the Net CRF observation goal is: (1.2 Wm -2 /K) * (0.2K/decade) = 0.24 Wm -2 /decade 10

45 CLARREO Requirements for Cloud Feedback CLARREO/CERES Calibration Requirement For Measuring Cloud Feedback The Net CRF observation goal sets the decadal calibration goal: Net CRF = SW CRF + LW CRF CRF = Clear minus All-Sky TOA Flux Shortwave (SW): 0.24/50 = 0.5% (2σ) Longwave (LW): 0.24/30 = 0.8% (2σ) This requirement is four times more accurate than the current SW broadband channel absolute accuracy: Requires overlap for current observations (no gap) and/or Requires CLARREO for future observations (gap OK) 11

46 CLARREO Requirements for Cloud Feedback CLARREO Sampling Requirement The Net CRF observation goal also sets the sampling requirements 20+ year record for trend to exceed natural variability Full swath sampling for low observation sampling noise 20km FOV or smaller to separate clear and cloud scenes Solution: CLARREO required to calibrate broadband observations to needed absolute accuracy. CERES provides sampling of the Net CRF decadal change. 12

47 CLARREO Requirements for Cloud Feedback Additional Climate Feedbacks: Similar climate model and data sampling analyses could be performed for other climate feedbacks Water vapor/lapse rate feedback will require latitude profile and height profile requirements for temperature and humidity. Can be extended to spectral fingerprinting. Surface albedo (e.g. snow/ice) will require latitude dependent requirements Other feedbacks could also be considered in this framework. Climateprediction.net perturbed physics modeling provides an ideal framework to explore the relationships. 13

48 CLARREO Requirements for Cloud Feedback The Quest Has Just Begun A new era of climate Observing System Simulation Experiments (OSSEs), a new era of calibration. A new methodology for linking climate model uncertainties to observation requirements has been highlighted. The current large uncertainties in climate feedbacks are not inevitable, nor is large uncertainty in climate sensitivity. CLARREO and CERES will likely play a key role. The example of cloud feedback linked to Net CRF does NOT eliminate the need to separately determine aerosol indirect effect. This remains the largest radiative forcing uncertainty and must be subtracted from the observed decadal change in SW CRF. 14