Why modelers should care about field projects
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1 Why modelers should care about field projects Michael Tjernström Department of Meteorology & Bolin Centre for Climate Research Stockholm University Sweden
2 Data without a model is chaos but a model without data is guesswork (Patrick Crill, Stockholm Uni.) Expanding our knowledge reveal things we didn t know already Reveal process relationships understanding the system to improve model formulations Evaluate models in different ways / Michael Tjernström, MISU
3 In the central Arctic, almost all in situ atmospheric observations are field data, one way or the other Surface obs Aircraft obs Soundings / Michael Tjernström, MISU Buoys &ships
4 / Michael Tjernström, MISU
5 Data without a model is chaos but a model without data is guesswork (Patrick Crill, Stockholm Uni.) Expanding our knowledge reveal things we didn t know already Reveal process relationships understanding the system to improve model formulations Evaluate models in different ways / Michael Tjernström, MISU
6 Expanding knowledge: Mixed-phase clouds in cold climates Temperature (K) / Michael Tjernström, MISU
7 Vertical thermal structure ~400 m ~300 m ~300 m ~500 m / Michael Tjernström, Stockholm University
8 Surface inversion Winter Spring Summer Autumn Surface 53% 15% 9% 61% Lifted 47% 85% 91% 39% Lifted inversion Boundary layer / Michael Tjernström Tjernström & Graversen 2009
9 Vertical thermal structure / Michael Tjernström, Stockholm University Tjernström et al. 2012
10 AOE-2001 ASCOS Moisture invesions / Michael Tjernström, MISU
11 CloudSat limit Cloud base < 200 m Summer cloud conditions / Michael Tjernström, Stockholm University CloudSat limit Cloud top < 1 km Tjernström et al. 2012
12 500 Clod top to inversion base (m) Frequency (%) / Michael Tjernström, MISU Sedlar et al. 2012
13 Aerosols, CCN and cloud interactions / Michael Tjernström, MISU Mauritsen et al. 2011
14 Optically thin clouds / Michael Tjernström, MISU
15 Optically thin clouds arosol/cloud interactions / Michael Tjernström, Stockholm University Birch et al. 2012
16 g Θ Ri = Θ z 2 U z Richardson number, Ri / Michael Tjernström, Stockholm University Day of August 2008
17 Cloud generated turbulent layer Partial decoupling layer Surface based boundary layer / Michael Tjernström, Stockholm University
18 Data without a model is chaos but a model without data is guesswork (Patrick Crill, Stockholm Uni.) Expanding our knowledge reveal things we didn t know already Reveal process relationships understanding the system to improve model formulations Evaluate models in different ways / Michael Tjernström, MISU
19 Process relationships: understanding the system Non-melt season: Variable T s < 0 C Melt season: Fixed T s 0 C / Michael Tjernström, Stockholm University Persson 2011
20 SHEBA Polar Night / Michael Tjernström, Stockholm University Courtesy of Persson 2012
21 The effects of clouds on the surface energy balance 27 May, 2010 / Michael Tjernström, Stockholm University Sedlar et al. 2010
22 Dependences in cloud forcing 27 May, 2010 / Michael Tjernström, Stockholm University Sedlar et al. 2010
23 Data without a model is chaos but a model without data is guesswork (Patrick Crill, Stockholm Uni.) Expanding our knowledge reveal things we didn t know already Reveal process relationships understanding the system to improve model formulations Evaluate models in different ways / Michael Tjernström, MISU
24 / Michael Tjernström, MISU Tjernström et al. 2012
25 Climate & bias? Polar MM5 RCA / Michael Tjernström, MISU
26 First order approximation: U * /U 10 ~ C D 1/2 F H /U 10 ~ - C H (T 2 T s ) F Q /U 10 ~ - C H (q 2 q s (T s )) / Michael Tjernström, MISU Tjernström et al. 2005
27 Shit in, shit out? / Michael Tjernström, MISU
28 / Michael Tjernström, MISU Compensating errors in radiation
29 Clouds in regional models / Michael Tjernström, Stockholm University Tjernström et al. 2008
30 Clouds in regional models Too low optical thickness in longwave calculations Too high optical thickness in shortwave calculations 25/06/ May, 2010 / Michael Tjernström, Stockholm University Tjernström et al. 2008
31 Two versions of the Arctic System Reanalysis, (ASR) Polar-WRF driven by ERA Interim, and ERA-Interim by itself / Michael Tjernström, MISU Wesslèn et al. 2013
32 / Michael Tjernström, MISU Wesslèn et al. 2013
33 Vertical profiles ASR-1 ASR-2 ERA-I / Michael Tjernström, MISU Wesslèn et al. 2013
34 Cloud properties / Michael Tjernström, MISU Wesslèn et al. 2013
35 / Michael Tjernström, MISU Wesslèn et al. 2013
36 / Michael Tjernström, MISU Wesslèn et al. 2013
37 Some thoughts A poor model will always be a poor model, no matter how much data assimilation is used. Poor models do poor forecasts eventually A good model is a model with a realistic climate for the right reasons. To make a good model requires an inevitable empirical component Even if a model can be evaluated against routine observations, a correct result may still come from compensating errors in different processes; hence the need to evaluate processes Special Challenges: Stable winter boundary layers Mixed phase clouds Optically thin clouds and aerosols / Michael Tjernström, MISU
38 Some recommendations Close collaboration between modeling and observational centers - and the academic community, using the best characteristics of each group For the PPP: Start with a canvas of what good field experiment data is already available and set up a series of modeling experiments do it now! Longer term field observations in the Arctic are extremely expensive and very demanding and it is difficult for the academic community to access the levels of policymaking where decisive funding action can be taken; national met centers have a more direct access to government let s scratch each other s back International coordination is key! / Michael Tjernström, MISU
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